Academia.eduAcademia.edu

Outline

Title
Abstract
Figures
Introduction
Related Work
THE Projection Method in Bipartite Networks
Proposed Method
The Proposed Link Prediction Method
Algorithm of the Proposed Method
Results
Dataset
EXPERIMENTAL Results and Evaluations
Conclusions
References
All Topics
Computer Science
Information Systems

Topic recommendation for authors as a link prediction problem

Profile image of Serpil ASLANSerpil ASLAN

2018, Future Generation Computer Systems

https://doi.org/10.1016/J.FUTURE.2018.06.050
visibility

…

description

28 pages

link

1 file

Abstract

Recently, link prediction studies on large-scale and complex networks have particularly become the focus of interest for researchers in various scientific fields. Many complex networks created from the real-world data contain bipartite structure by nature. Bipartite networks are a kind of complex networks that represent the interactions between the different node groups. Almost all of the previous studies on link prediction in bipartite networks focus on using the properties of projection networks to predict the relations between the node pairs. In this study, a novel similarity-based link prediction method based on strengthening weighted projection is proposed to predict the potential links between the authors and the topics in the large-scale bipartite academic information network created from the real-world data. Since information loss occurs when bipartite networks are converted to unimodal networks, it should be noted that when making a link prediction in this paper, both the bipartite network and the information on strengthening unimodal network obtained from the bipartite network are used. To evaluate the proposed method, a bipartite network was first created from a real data set consisting of authors and their work. Then the method was tested on this network. Experimental results demonstrate that it is possible to obtain faster and more accurate link prediction results by the proposed method.

Figures (19)
Fig. 1. Prediction of links at time ¢+/ in bipartite network for an author-topic bipartite network at time f¢. In this figure, the circular nodes at the top represent authors and the square nodes at the bottom represent the topics, and the solid and dashed lines respectively represent already existent links and the prediction links between authors and topics. Fig. 1 shows prediction of links in an author-topic bipartite network. The solid and dashed line respectively denotes the fact that the author already studied the topic tag and the prediction that the author will study the topic tag. The main task of link prediction is to predict the possible links (dashed lines) in the author-topic network at time t+J/ according to the link information (solid lines) observed at time f.
Fig. 1. Prediction of links at time ¢+/ in bipartite network for an author-topic bipartite network at time f¢. In this figure, the circular nodes at the top represent authors and the square nodes at the bottom represent the topics, and the solid and dashed lines respectively represent already existent links and the prediction links between authors and topics. Fig. 1 shows prediction of links in an author-topic bipartite network. The solid and dashed line respectively denotes the fact that the author already studied the topic tag and the prediction that the author will study the topic tag. The main task of link prediction is to predict the possible links (dashed lines) in the author-topic network at time t+J/ according to the link information (solid lines) observed at time f.
Fig. 2. (a) The bipartite network G=(U,V,E), as well as its U-projection unimodal network (b) and V-projection unimodal network (c). In this figure, the circular nodes at the top represent authors and the square nodes at the bottom represent the topics. The edge weight in (b) and (c) is set as according to Definition-3. When bipartite networks are converted to unimodal networks, some information hidden in the topological structure of the network may lose their importance. To prevent this situation, weighted bipartite networks are used. Weighted networks are common in many areas, however, they are particularly important when representing the topological properties of the network in bipartite networks such as author-article relations, patient-disease, and actor-movie. In ¢ unimodal author-author network extracted from the bipartite network containing the authors and their publications, an edge weight represents the number of publications two authors have made collaboratively in the bipartite network. Weighted unimodal network projection is a technique used for obtaining a unimodal network from a two dimensional network; where the edge weight represents the number of common neighbors of the corners, and the network obtainec by using a weighted unimodal projection is called the weighted projection network. Mathematically it can be defined as follows: Ey = {(ui, u;)| Uj, Uj E U, Av; E V, VLE P(u;) fa) T(u,)}, Es = {(v;,v;)| Vj, V0; E V, Au; E U, Uj; E rv) N T(v;)}.
Fig. 2. (a) The bipartite network G=(U,V,E), as well as its U-projection unimodal network (b) and V-projection unimodal network (c). In this figure, the circular nodes at the top represent authors and the square nodes at the bottom represent the topics. The edge weight in (b) and (c) is set as according to Definition-3. When bipartite networks are converted to unimodal networks, some information hidden in the topological structure of the network may lose their importance. To prevent this situation, weighted bipartite networks are used. Weighted networks are common in many areas, however, they are particularly important when representing the topological properties of the network in bipartite networks such as author-article relations, patient-disease, and actor-movie. In ¢ unimodal author-author network extracted from the bipartite network containing the authors and their publications, an edge weight represents the number of publications two authors have made collaboratively in the bipartite network. Weighted unimodal network projection is a technique used for obtaining a unimodal network from a two dimensional network; where the edge weight represents the number of common neighbors of the corners, and the network obtainec by using a weighted unimodal projection is called the weighted projection network. Mathematically it can be defined as follows: Ey = {(ui, u;)| Uj, Uj E U, Av; E V, VLE P(u;) fa) T(u,)}, Es = {(v;,v;)| Vj, V0; E V, Au; E U, Uj; E rv) N T(v;)}.
Definition 4. (Strengthening of Bipartite Projection (SBP%)): From each of the U and V node par s forming G =(U,V,E) bipartite network, two unimodal networks, which are U-projection network G,, = (U, E, projection G, = (U,E,) are obtained. (A,B) € E,W: (A,B) > |T(A) NT(B)I. If W(A, B)>a, while the (A, B) is kept in the strengthening network SBP®, the edges whose W(A,B) < a weights are below the value are removed from the strengthening network (a: is the predetermined threshold value). This network which is the backbone of the unimodal network, in which strong node pairs are preserved pairs are removed, is obtained. For this case, more concrete analyzes can be made by using a weigh ie exnlained in Fio 2 he give and V edge o hreshol way, a strengthene and the weak nod function. This cas Fig. 3. (a) SBP tno Of the author-author projected network which is given in Fig. 2(a) for a@ = 1. (b) SBP ionic of the topic-topic projected network which is given in Fig. 2(c) for a =1. SBP no, and SBP ic strengthened network was constructed according to Definition 4.
Definition 4. (Strengthening of Bipartite Projection (SBP%)): From each of the U and V node par s forming G =(U,V,E) bipartite network, two unimodal networks, which are U-projection network G,, = (U, E, projection G, = (U,E,) are obtained. (A,B) € E,W: (A,B) > |T(A) NT(B)I. If W(A, B)>a, while the (A, B) is kept in the strengthening network SBP®, the edges whose W(A,B) < a weights are below the value are removed from the strengthening network (a: is the predetermined threshold value). This network which is the backbone of the unimodal network, in which strong node pairs are preserved pairs are removed, is obtained. For this case, more concrete analyzes can be made by using a weigh ie exnlained in Fio 2 he give and V edge o hreshol way, a strengthene and the weak nod function. This cas Fig. 3. (a) SBP tno Of the author-author projected network which is given in Fig. 2(a) for a@ = 1. (b) SBP ionic of the topic-topic projected network which is given in Fig. 2(c) for a =1. SBP no, and SBP ic strengthened network was constructed according to Definition 4.
The number of patterns covered by a PL, is directly related to the proximity of the edge nodes constituting it to each other in a bipartite network. The greater the number of patterns covered by the PL, the higher the probability of PL to occur in the future. Therefore, the number of patterns covered by PL can be used to measure the prediction probability of the notential link. Fig. 6. (a) The bipartite author-topic network, (b) The bipartite author-topic training network, (c) The bipartite author-topic test network. For example, in the bipartite network given in Fig. 6(a), the circular nodes at the top represent authors and the quare nodes at the bottom represent the topic tags the authors studied. T2,74 are the common study topics for the B ind E authors, (E,T1) and (£,7T3) are the potential links belonging to the PL set meeting the Definition-5 condition
The number of patterns covered by a PL, is directly related to the proximity of the edge nodes constituting it to each other in a bipartite network. The greater the number of patterns covered by the PL, the higher the probability of PL to occur in the future. Therefore, the number of patterns covered by PL can be used to measure the prediction probability of the notential link. Fig. 6. (a) The bipartite author-topic network, (b) The bipartite author-topic training network, (c) The bipartite author-topic test network. For example, in the bipartite network given in Fig. 6(a), the circular nodes at the top represent authors and the quare nodes at the bottom represent the topic tags the authors studied. T2,74 are the common study topics for the B ind E authors, (E,T1) and (£,7T3) are the potential links belonging to the PL set meeting the Definition-5 condition
The covered patterns by a potential link (PL) are the edges in the projection network. The A and B edges havin; common neighbor nodes in the G bipartite network are represented by the (A, B) edge in the G,, projection network Some topological information is lost when bipartite networks are converted to unimodal networks. Although th bipartite network given in Fig. 7(a) and Fig. 7(c) are different from each other, their projection networks are the sam as can be seen in Fig. 7(b) and Fig. 7(d). In such cases, there will be a serious topological information loss in th structure of the bipartite network, in order to deal with this problem, a weighted bipartite network is created to protec the network information. Since the number of common neighbors of the A and B nodes in Fig. 7(a) is lesser than th number of neighbors in Fig. 7(b). it is graded smaller. Fig. 7. Two different bipartite networks and theirs weighted projection networks. (b) The projection network of bipartite network which is given in (a). (d) The projection network of bipartite network which is given in (c).
The covered patterns by a potential link (PL) are the edges in the projection network. The A and B edges havin; common neighbor nodes in the G bipartite network are represented by the (A, B) edge in the G,, projection network Some topological information is lost when bipartite networks are converted to unimodal networks. Although th bipartite network given in Fig. 7(a) and Fig. 7(c) are different from each other, their projection networks are the sam as can be seen in Fig. 7(b) and Fig. 7(d). In such cases, there will be a serious topological information loss in th structure of the bipartite network, in order to deal with this problem, a weighted bipartite network is created to protec the network information. Since the number of common neighbors of the A and B nodes in Fig. 7(a) is lesser than th number of neighbors in Fig. 7(b). it is graded smaller. Fig. 7. Two different bipartite networks and theirs weighted projection networks. (b) The projection network of bipartite network which is given in (a). (d) The projection network of bipartite network which is given in (c).
Table 1: Classical neighborhood-based link prediction algorithms As can be understood from Equation 2, the smaller the degrees of the common neighbors of the A and B nodes, the greater the pattern weight. The probability of formation of each pattern element covered by a PL is equivalent to the total weight of the patterns it covers. The final score of the (A, p;) potential link that is predicted to be formed in the Gtrain = (U,V, E¢rqin) training network is as follows: In Table 1, some of the frequently used neighborhood-based link prediction algorithms are formulated, such as the degree of the node k,, A in the bipartite network (node degree=number of neighbors) and the neighbors of the (A), A node in the bipartite network.
Table 1: Classical neighborhood-based link prediction algorithms As can be understood from Equation 2, the smaller the degrees of the common neighbors of the A and B nodes, the greater the pattern weight. The probability of formation of each pattern element covered by a PL is equivalent to the total weight of the patterns it covers. The final score of the (A, p;) potential link that is predicted to be formed in the Gtrain = (U,V, E¢rqin) training network is as follows: In Table 1, some of the frequently used neighborhood-based link prediction algorithms are formulated, such as the degree of the node k,, A in the bipartite network (node degree=number of neighbors) and the neighbors of the (A), A node in the bipartite network.
The degrees of A and B nodes have a negative effect on the {A, B} pattern weight. For example, the degrees of the 4 and B nodes in Fig. 8(a) are respectively 2 and 1. In Fig. 8(b), the degrees of the A and B nodes are respectively 2 and 3. In Fig. 8(a), while the A author studied the T1 and T2 topics, the B authors only studied the T2 topic. However, in Fig. 8(b), they have carried out many different studies, and their only common study field is T2. Therefore, the study fields of the academicians in Fig. 8(b), are less similar than that of the authors in Fig. 8(a), thus, the {A, B} weight should be higher in Fig. 8(a).
The degrees of A and B nodes have a negative effect on the {A, B} pattern weight. For example, the degrees of the 4 and B nodes in Fig. 8(a) are respectively 2 and 1. In Fig. 8(b), the degrees of the A and B nodes are respectively 2 and 3. In Fig. 8(a), while the A author studied the T1 and T2 topics, the B authors only studied the T2 topic. However, in Fig. 8(b), they have carried out many different studies, and their only common study field is T2. Therefore, the study fields of the academicians in Fig. 8(b), are less similar than that of the authors in Fig. 8(a), thus, the {A, B} weight should be higher in Fig. 8(a).
Fig. 9. A section of the author-topic bipartite network. In this figure, the circular nodes which is colored in red at the top represent authors and the square nodes which is colored in blue at the bottom represent the topics. "author names, article title, keywords, abstract and reference list" are saved to our database. The collected information was only used in the academic study without any commercial purpose. The collected data is raw data. Since the network we will create from the raw data will have useless information, it was first examined with data processing techniques. For example, only the authors with one publication were filtered in the 2000-2016 time period. The standardization method mentioned in subsection 4.1 is applied for the topic titles. A bipartite network of author-topic title is created from the obtained database. In 2000-2016 G = (U,V,E) bipartite network, in 2000-2011 Gi-gin = (U,V, Etrain) and 2012-2016 Gres¢ = (U,V, Exest) bipartite networks are created. The G bipartite network consists of 67.677 authors and 122.852 topic nodes, and 1.260.559 links that these nodes create.
Fig. 9. A section of the author-topic bipartite network. In this figure, the circular nodes which is colored in red at the top represent authors and the square nodes which is colored in blue at the bottom represent the topics. "author names, article title, keywords, abstract and reference list" are saved to our database. The collected information was only used in the academic study without any commercial purpose. The collected data is raw data. Since the network we will create from the raw data will have useless information, it was first examined with data processing techniques. For example, only the authors with one publication were filtered in the 2000-2016 time period. The standardization method mentioned in subsection 4.1 is applied for the topic titles. A bipartite network of author-topic title is created from the obtained database. In 2000-2016 G = (U,V,E) bipartite network, in 2000-2011 Gi-gin = (U,V, Etrain) and 2012-2016 Gres¢ = (U,V, Exest) bipartite networks are created. The G bipartite network consists of 67.677 authors and 122.852 topic nodes, and 1.260.559 links that these nodes create.
Fig. 11. The comparison of the precision values at different thresholds of the algorithms
Fig. 11. The comparison of the precision values at different thresholds of the algorithms
Fig. 12. The comparison of the Precision-recall curves at different thresholds of the algorithms.
Fig. 12. The comparison of the Precision-recall curves at different thresholds of the algorithms.
In Fig. 10 and Fig. 11, the best prediction quality for each algorithm is obtained from the first version link prediction set (PL) obtained by the a = 8 threshold value of the proposed method. As can be seen in Fig. 10 and Fig. 11, the prediction quality of the algorithms decreases as the hreshold value increases. However, the potential link set grows as the threshold value becomes smaller as indicated in Table 2, and parallel to this, the run time and prediction quality of the algorithm increases. As the threshold value academic backgrounds are most similar to each other are ana increases, the relationships between the authors whose yzed by our method, and the topic suggestions are made. However, as can be seen in the above figures, this situation will actually have a negative effect, and then cause information loss. In Fig. 10, the AUC prediction quality in al value increases. It is clearly indicated by this tendency that algorithms tends to slightly decrease when the threshold here is hidden information in the network topology that can be used for prediction purposes. As the threshold value increases, the training set decreases, this results in a reduction in the number of node pairs with common neigh| bors, and thus, cause the difficulty of link prediction to increase. For this reason, links with high probability that do not exist in the network due to information loss cannot be predicted. This causes a decrease in the performance. The problem of choosing an appropriate threshold value is a major problem. As expressed in the study in [34], the most frequently observed edge weight is in the projection network. In our database this value is found as 10. The first results a = 10 were calculated according to the threshold value. It can be observed that, later, as the threshold value is exceeded, the prediction quality drops, however, the PL set decreases and run time increases, the prediction quality increases if it falls below the threshold value, however, the PL set grows and run time increases. As can be seen in Fig. the proposed algorithm in the a = 8 value, link prediction 10(a) and Fig. 11(a), according to the quality criterion of quality value is 0.98, according to the Precision quality criterion it is 1. Since the prediction quality has high values in the a = 8 threshold value, the method could not be tested for thresholds with lower values. Tha nasality Al the -avaAiatian palaalatad hy Dreanicotan. ranall aia axralivahaw matrin. an tha anatandial Lal eat Table 2: The number of potential link set created by our method according to threshold values
In Fig. 10 and Fig. 11, the best prediction quality for each algorithm is obtained from the first version link prediction set (PL) obtained by the a = 8 threshold value of the proposed method. As can be seen in Fig. 10 and Fig. 11, the prediction quality of the algorithms decreases as the hreshold value increases. However, the potential link set grows as the threshold value becomes smaller as indicated in Table 2, and parallel to this, the run time and prediction quality of the algorithm increases. As the threshold value academic backgrounds are most similar to each other are ana increases, the relationships between the authors whose yzed by our method, and the topic suggestions are made. However, as can be seen in the above figures, this situation will actually have a negative effect, and then cause information loss. In Fig. 10, the AUC prediction quality in al value increases. It is clearly indicated by this tendency that algorithms tends to slightly decrease when the threshold here is hidden information in the network topology that can be used for prediction purposes. As the threshold value increases, the training set decreases, this results in a reduction in the number of node pairs with common neigh| bors, and thus, cause the difficulty of link prediction to increase. For this reason, links with high probability that do not exist in the network due to information loss cannot be predicted. This causes a decrease in the performance. The problem of choosing an appropriate threshold value is a major problem. As expressed in the study in [34], the most frequently observed edge weight is in the projection network. In our database this value is found as 10. The first results a = 10 were calculated according to the threshold value. It can be observed that, later, as the threshold value is exceeded, the prediction quality drops, however, the PL set decreases and run time increases, the prediction quality increases if it falls below the threshold value, however, the PL set grows and run time increases. As can be seen in Fig. the proposed algorithm in the a = 8 value, link prediction 10(a) and Fig. 11(a), according to the quality criterion of quality value is 0.98, according to the Precision quality criterion it is 1. Since the prediction quality has high values in the a = 8 threshold value, the method could not be tested for thresholds with lower values. Tha nasality Al the -avaAiatian palaalatad hy Dreanicotan. ranall aia axralivahaw matrin. an tha anatandial Lal eat Table 2: The number of potential link set created by our method according to threshold values
it is possible to obtain fast and high-quality link prediction results from a large-scale bipartite Experimental results show that, through the proposed method, it is possible to obtain fast and high- quality link prediction results from a large-scale network created with real-world data.
it is possible to obtain fast and high-quality link prediction results from a large-scale bipartite Experimental results show that, through the proposed method, it is possible to obtain fast and high- quality link prediction results from a large-scale network created with real-world data.

Related papers

The link prediction problem in bipartite networks
Jérôme Kunegis

Computational Intelligence for …, 2010

We define and study the link prediction problem in bipartite networks, specializing general link prediction algorithms to the bipartite case. In a graph, a link prediction function of two vertices denotes the similarity or proximity of the vertices. Common link prediction functions for general graphs are defined using paths of length two between two nodes. Since in a bipartite graph adjacency vertices can only be connected by paths of odd lengths, these functions do not apply to bipartite graphs. Instead, a certain class of graph kernels (spectral transformation kernels) can be generalized to bipartite graphs when the positivesemidefinite kernel constraint is relaxed. This generalization is realized by the odd component of the underlying spectral transformation. This construction leads to several new link prediction pseudokernels such as the matrix hyperbolic sine, which we examine for rating graphs, authorship graphs, folksonomies, document-feature networks and other types of bipartite networks.

View PDFchevron_right
Utilizing adjacency of colleagues and type correlations for enhanced link prediction
현지 정

2020

Discoveries of new relationships in the network of objects have been required in various applications such as social networks, DBLP bibliographic networks and biological networks. Specifically, link prediction in heterogeneous information networks (HINs) that consist of multiple types of nodes and links has received much attention recently because many information networks of the real world are HINs. We observe various factors that affect the existence of a link in HINs. Firstly, certain structural characteristics of nodes whose types are the same as that of a source (or target) node give important information for link prediction. Secondly, in the HINs, there can be meaningful correlation between links of a particular link type and paths of a particular path type (also called a meta-path). In other words, paths of different path types affect the existence of links differently. Finally, we use the number of paths between source and target nodes to measure proximity of two nodes. Based on these observations, we newly propose several features and a prediction model. We show through various experiments that our proposed method works effectively and performs better than the other existing methods.

View PDFchevron_right
Link Prediction in a Modified Heterogeneous Bibliographic Network
Henry Adorna

2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 2012

Researchers have discovered, in recent years, the advantages of modeling complex systems using heterogeneous information networks. These networks are comprised of heterogeneous sets of nodes and edges that better represent the different entities and relationships often found in the real world. Although heterogeneous networks provide a richer semantic view of the data, the added complexity makes it difficult to directly apply existing techniques that work well on homogeneous networks.

View PDFchevron_right
Experiments with Link Prediction on DBLP coauthorship network
onkar bhardwaj

We study the link prediction problem on coauthor-ship network induced by DBLP dataset. Each author is modeled as a node and an edge represents coauthorship. We evaluate the performance of well known predictors on this network and show that the coauthorship is not random. We also study the link prediction problem using the community structure of the DBLP network and show a counterintuitive result that most of the edges that keep getting added are not intra-community edges. We contrast this result against the high clustering coefficient of the network using a simple observation that most of the papers are coauthored by more than three authors. Finally, we show that the new incoming nodes are more likely to collaborate with prolific authors.

View PDFchevron_right
Strength-Based Link Prediction in Scientific Bibliographic Networks
Abdelhak Boubetra

Journal of Information Technology Research

Network analysis literature counts plenty of models of different paradigms designed for solving the link prediction problem in complex information networks. However, fewer studies that have exploited link strength-related social theories for this purpose even in a social context. In this paper, the authors introduce a new approach to solve the link prediction problem in scientific bibliographic networks. The aim is to predict future collaboration relations between scientists relying upon the “strength of strong ties” hypothesis. The proposed model estimates the strength of a relation between two scientists using a set of efficient link strength indicators. The importance of the relation is then validated according to the scientists&#39; expected collaboration strategies. The prediction process is performed in a heterogeneous context where the types of the nodes and the links are considered. Experiments on the DBLP real-world scientific bibliographic network, show higher performance ...

View PDFchevron_right
Trend Based Vertex Similarity for Academic Collaboration Recommendation
tin huynh

Lecture Notes in Computer Science, 2013

In this paper, we propose a new method used for collaboration recommendation in the academic domain. The proposed method is based on combination of probability theory and graph theory for modeling and analysing co-author network. In the co-author network, similar vertices are explored as potential candidates for collaboration recommendation. Taking the trend information into considering similarity of vertices in the network is the main contribution of this research. We did experiments with co-author networks extracted from the DBLP. Coauthorship that will occur in future used to evaluate accuracy of collaboration recommendation methods. We used metadata of publications from 2001 to 2005 for building the training network. The testing networks were built with publications from 2006-2008 (the testing network 1 for the near future prediction) and 2009-2011 (the testing network 2 for the far future prediction). The experimental results show that the proposed method, called TBRSS (Trend Based Relation Strength Similarity), outperforms other existing methods.

View PDFchevron_right
Time-aware link prediction based on strengthened projection in bipartite networks
Serpil ASLAN

Information Sciences, 2019

The traditional projection models in the bipartite networks involve many node pairs that consist of weak relationships. These node pairs lead to poor quality predictions as well as high computation time. In this paper, to cope with these problems, we firstly propose a novel projection model, which is called "Strengthened Projection Model". Then, to predict the potential links in the future, we present a new link prediction approach based on the proposed projection model. Thanks to the proposed model, the computation time is shortened, and the high probability predictions are extracted. The majority of the previous works conducted in this area have used the classical proximity measure algorithms that only take into account the current network structure, regardless of when events occur in the network evolution. To overcome these limited methods, in this paper, we also propose a novel proximity measure algorithm that considers the bipartite network evolution. To the best of our knowledge, this is the first attempt that takes into account the timeawareness in bipartite networks. To evaluate the performance of our proposed approach, we conducted experiments on the academic information network. To construct this bipartite network, we collected data from IEEE Xplore. The experimental results show that the success of the proposed method is promising.

View PDFchevron_right
A Unified Link Prediction Framework for Predicting Arbitrary Relations in Heterogeneous Academic Networks
JEAN PIERRE BANCELIN

IEEE Access, 2019

Most of the existing link prediction methods for heterogeneous academic networks can only predict one or two specific relation types rather than arbitrary relation types. Although several recently proposed methods have involved multi-relational prediction problems, they do not comprehensively consider the rich semantic or temporal information of heterogeneous academic networks. Considering that researchers may have diverse requirements for different types of academic resources, in this study, we propose a new unified link prediction framework (UniLPF) for arbitrary types of academic relations. First, a weighted and directed heterogeneous academic network containing rich academic objects and relations is constructed. Then, an automatic meta-path searching method is proposed to extract the meta-paths for arbitrary prediction tasks. Two meta-path based object similarity measures combining temporal information and content relevance are also proposed to measure the features of the meta-paths. Finally, a pervasive link prediction model is built, which can be embodied based on an arbitrarily specified prediction task and the corresponding meta-path features. Extensive experiments for predicting various relation types with practical significance are conducted on a large-scale Microsoft Academic dataset. The experimental results demonstrate that our proposed UniLPF framework can predict arbitrary specified academic relations, and outperforms the comparison methods in terms of F-measure, accuracy, AUC and ROC. In addition, the time scalability experiments prove that UniLPF also achieves good performance for predicting the academic relations over time.

View PDFchevron_right
Using Link Prediction to Estimate the Collaborative Influence of Researchers
Roberto Cesar

2013 IEEE 9th International Conference on e-Science, 2013

The influence of a particular individual in a scientific collaboration network could be measured in several ways. Estimating influence commonly requires calculating computationally costly global measures, which may be impractical on networks with hundreds of thousands of vertices. In this paper, we introduce new local measures to estimate the collaborative influence of individual researchers in a collaboration network. Our approach is based on the link prediction technique, and its underlying rationale is to assess how the presence/absence of a researcher affects the link prediction outcome in the network as a whole. It is natural to assume that the absence of a researcher with strong influence in the network will cause negative impact in the correct link prediction. Scientists are represented as vertices in the collaboration graph, and a vertex removal and corresponding link prediction process are performed iteratively for all vertices, each vertex being handled independently. The SVM supervised learning model has been adopted as link predictor. The proposed approach has been tested on real collaboration networks relative to multiple time periods, processing the networks in order to assign more relevance to recent than to older collaborations. The experimental tests suggest that our measure of impact on link prediction has high negative correlation with standard vertex importance measures such as betweenness and closeness centrality.

View PDFchevron_right
Co-authorship prediction in academic social network
Luciano A Digiampietri

2016

The prediction of relationships in a social network is a complex and extremely useful task to enhance or maximize collaborations by indicating the most promising partnerships. In academic social networks, prediction of relationships is typically used to try to identify potential partners in the development of a project and/or co-authors for publishing papers. This paper presents an approach to predict coauthorships combining artificial intelligence techniques with the state-of-the-art metrics for link predicting in social networks.

View PDFchevron_right

References (63)

  1. A.L. Barabási. Network science. Cambridge university press, 2016.
  2. S. Wasserman and K. Faust. Social network analysis: Methods and applications. Vol. 8. Cambridge university press, 1994.
  3. D. Corlette and F. M. Shipman III. "Link prediction applied to an open large-scale online social network." Proceedings of the 21st ACM conference on Hypertext and hypermedia. ACM, 2010, p. 135-140.
  4. D. Liben Nowell and J. Kleinberg. "The link prediction problem for social networks." journal of the Association for Information Science and Technology 58.7 (2007) 1019-1031.
  5. Y. Yang, R. N. Lichtenwalter, and N. V. Chawla. "Evaluating link prediction methods." Knowledge and Information Systems 45.3 (2015) 751-782.
  6. S.Y. Hwang, C. P. Wei, and Y. F. Liao. "Coauthorship networks and academic literature recommendation." Electronic Commerce Research and Applications 9.4 (2010) 323-334.
  7. E. Bütün, M. Kaya, and R. Alhajj. "A Supervised Learning Method for Prediction Citation Count of Scientists in Citation Networks." Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017. ACM, 2017, p. 952-958.
  8. V. E. Krebs. "Mapping networks of terrorist cells." Connections 24.3 (2002) 43-52.
  9. S. Scellato, A. Noulas, and C. Mascolo. "Exploiting place features in link prediction on location-based social networks." Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2011, p. 1046-1054.
  10. S. Huang et al. "Social friend recommendation based on multiple network correlation." IEEE transactions on multimedia 18.2 (2016) 287-299.
  11. E. A. Leicht, P. Holme, and M. EJ Newman. "Vertex similarity in networks." Physical Review E 73.2 (2006) 026120.
  12. F. Folino and C. Pizzuti. "A comorbidity-based recommendation engine for disease prediction." Proceedings of the 23rd International Symposium on Computer-Based Medical Systems. CBMS, IEEE, 2010, p. 6-12.
  13. S. Gül, M. Kaya and B. Kaya. "Predicting links in weighted disease networks." Proceedings of the 3rd International Conference on Computer and Information Sciences. ICCOINS, IEEE, 2016, p. 77-81.
  14. L. Lü and T. Zhou. "Link prediction in complex networks: A survey." Physica A: statistical mechanics and its applications 390.6 (2011) 1150-1170.
  15. R. R. Sarukkai. "Link prediction and path analysis using Markov chains1." Computer Networks 33.1-6 (2000) 377-386.
  16. J. Zhu, J. Hong, and J. G. Hughes. "Using markov chains for link prediction in adaptive web sites." Proceedings of the Soft- Ware 2002 on Computing in an Imperfect World. Soft-ware 2002, Springer, p. 60-73.
  17. B. Taskar et al. "Link prediction in relational data." Proceedings of the Advances in neural information processing systems. NIPS, 2004, p. 659-666.
  18. A. Popescul and L. H. Ungar. "Statistical relational learning for link prediction." Proceedings of the Workshop on learning statistical models from relational data. IJCAI, 2003.
  19. F. Guo, Z. Yang and T. Zhou. "Predicting link directions via a recursive subgraph-based ranking." Physica A: Statistical Mechanics and its Applications 392.16 (2013) 3402-3408.
  20. T. Zhou, L. Lü, and Y. C. Zhang. "Predicting missing links via local information." The European Physical Journal B71.4 (2009) 623-630.
  21. P. Pei, B. Liu, and L. Jiao. "Link prediction in complex networks based on an information allocation index." Physica A: Statistical Mechanics and its Applications 470 (2017) 1-11.
  22. A. Grabowski, N. Kruszewska, and R. A. Kosiński. "Dynamic phenomena and human activity in an artificial society." Physical Review E 78.6 (2008) 066110.
  23. M. Al Hasan et al. "Link prediction using supervised learning." Proceedings of the Workshop on link analysis, counter- terrorism and security. SDM06, 2006.
  24. R. N. Lichtenwalter, J. T. Lussier, and N. V. Chawla. "New perspectives and methods in link prediction." Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2010, p. 243-252.
  25. R. Laishram, K. Mehrotra, and C. K. Mohan. "Link Prediction in Social Networks with Edge Aging." Proceedings of the 28th International Conference on Tools with Artificial Intelligence. ICTAI, 2016, IEEE, p. 606-613.
  26. M. Latapy, C. Magnien, and N. D. Vecchio. "Basic notions for the analysis of large two-mode networks." Social networks 30.1 (2008) 31-48.
  27. D. B. Larremore, A. Clauset, and A. Z. Jacobs. "Efficiently inferring community structure in bipartite networks." Physical Review E 90.1 (2014) 012805.
  28. M. Gao et al. "Projection-based link prediction in a bipartite network." Information Sciences 376 (2017) 158-171.
  29. Y. J. Chang and H. Y. Kao. "Link prediction in a bipartite network using Wikipedia revision information." Proceedings of the 2012 Conference on Technologies and Applications of Artificial Intelligence. TAAI, 2012, IEEE, p. 50-55.
  30. A. Nigam and N. V. Chawla. "Link prediction in a semi-bipartite network for recommendation." Proceedings of the Asian Conference on Intelligent Information and Database Systems. ACIIDS, Springer, 2016, p. 127-135.
  31. X. Wang, Y. Liu, and F. Xiong. "Improved personalized recommendation based on a similarity network." Physica A: Statistical Mechanics and its Applications 456 (2016) 271-280.
  32. N. Benchettara, R. Kanawati, and C. Rouveirol. "Supervised machine learning applied to link prediction in bipartite social networks." Proceedings of the 2010 International Conference on Advances in Social Networks Analysis and Mining. ASONAM, IEEE, 2010, p. 326-330.
  33. Y. Luo, et al. "Predicting drug side effects based on link prediction in bipartite network." Proceeding of the 7th International Conference on Biomedical Engineering and Informatics. BMEI, IEEE, 2014, p. 729-733.
  34. Z. Neal. "The backbone of bipartite projections: Inferring relationships from co-authorship, co-sponsorship, co-attendance and other co-behaviors." Social Networks 39 (2014) 84-97.
  35. B. Derudder and P. Taylor. "The cliquishness of world cities." Global Networks 5.1 (2005) 71-91.
  36. J. W. Neal and Z. P. Neal. "The multiple meanings of peer groups in social cognitive mapping." Social Development 22.3 (2013) 580-594.
  37. H. Zhang et al. "Modeling the self-similarity in complex networks based on Coulomb's law." Communications in Nonlinear Science and Numerical Simulation 35 (2016) 97-104.
  38. G. J. Qi, C. C. Aggarwal, and T. S. Huang. "Breaking the barrier to transferring link information across networks." IEEE Transactions on Knowledge and Data Engineering 27.7 (2015) 1741-1753.
  39. A. Andalib and S. M. Babamir. "A class-based link prediction using Distance Dependent Chinese Restaurant Process." Physica A: Statistical Mechanics and its Applications456 (2016) 204-214.
  40. Z. Li et al. "Quantitative function and algorithm for community detection in bipartite networks." Information Sciences367 (2016) 874-889.
  41. X. Li and H. Chen. "Recommendation as link prediction in bipartite graphs: A graph kernel-based machine learning approach." Decision Support Systems 54.2 (2013) 880-890.
  42. Y. Li et al. "Name disambiguation in scientific cooperation network by exploiting user feedback." Artificial Intelligence Review 41.4 (2014) 563-578.
  43. D. J. Watts and S. H. Strogatz. "Collective dynamics of 'small-world'networks." nature 393.6684 (1998) 440.
  44. L. Ferreri, E. Venturino, and M. Giacobini. "Do diseases spreading on bipartite networks have some evolutionary advantage?" Proceedings of the European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics. EvoBIO, Springer, 2011, p. 141-146.
  45. Y. Zhang et al. "Network-based analysis reveals distinct association patterns in a semantic MEDLINE-based drug-disease- gene network." Journal of biomedical semantics 5.1 (2014) 33.
  46. B. Kaya and M. Poyraz. "Unsupervised link prediction in evolving abnormal medical parameter networks." International Journal of Machine Learning and Cybernetics 7.1 (2016) 145-155.
  47. B. Kaya and M. Poyraz. "Supervised link prediction in symptom networks with evolving case." Measurement 56 (2014) 231- 238.
  48. M. Ge, A. Li, and M. Wang. "A bipartite network-based method for prediction of long non-coding RNA-protein interactions." Genomics, proteomics & bioinformatics 14.1 (2016) 62-71.
  49. T. Zhou et al. "Bipartite network projection and personal recommendation." Physical Review E 76.4 (2007) 046115.
  50. J. Kunegis, E. W. De Luca, and S. Albayrak. "The link prediction problem in bipartite networks." Proceedings of the International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems. IPMU, Springer, 2010, p. 380-389.
  51. P. Zhang, A. Zeng, and Y. Fan. "Identifying missing and spurious connections via the bi-directional diffusion on bipartite networks." Physics Letters A 378.32-33 (2014) 2350-2354.
  52. B. Shams and S. Haratizadeh. "SibRank: Signed bipartite network analysis for neighbor-based collaborative ranking." Physica A: Statistical Mechanics and its Applications458 (2016) 364-377.
  53. O. Allali, C. Magnien, and M. Latapy. "Link prediction in bipartite graphs using internal links and weighted projection." Proceedings of the 2011 IEEE Conference on Computer Communications Workshops. INFOCOM WKSHPS, IEEE, 2011, p. 936-941.
  54. Y. Cui et al. "Heterogeneous Network Linkage-weight Based Link Prediction in Bipartite Graph for Personalized Recommendation." Procedia Computer Science 91 (2016) 953-958.
  55. E. Hoseini, S. Hashemi, and A. Hamzeh. "Link prediction in social network using co-clustering based approach." Proceedings of the 26th International Conference on Advanced Information Networking and Applications Workshops. WAINA, 2012, IEEE, p. 795-800.
  56. S. Banerjee, M. Jenamani, and D. K. Pratihar. "Properties of a Projected Network of a Bipartite Network." arXiv preprint arXiv:1707.00912 (2017).
  57. M. EJ. Newman. "The structure and function of complex networks." SIAM review 45.2 (2003) 167-256.
  58. K. A. Zweig and M. Kaufmann. "A systematic approach to the one-mode projection of bipartite graphs." Social Network Analysis and Mining 1.3 (2011) 187-218.
  59. Z. Neal. "Identifying statistically significant edges in one-mode projections." Social Network Analysis and Mining 3.4 (2013) 915-924.
  60. Z. P. Neal. "The duality of world cities and firms: comparing networks, hierarchies, and inequalities in the global economy." Global Networks 8.1 (2008) 94-115.
  61. Z. Zhang et al. "Efficient incremental dynamic link prediction algorithms in social network." Knowledge-Based Systems 132 (2017) 226-235.
  62. S. Liu et al. "Extended resource allocation index for link prediction of complex network." Physica A: Statistical Mechanics and its Applications 479 (2017) 174-183.
  63. J. L. Herlocker et al. "Evaluating collaborative filtering recommender systems." ACM Transactions on Information Systems (TOIS) 22.1 (2004) 5-53.

Related papers

Link Prediction in Co-authorship Networks
Diman Hassan

Science journal of University of Zakho, 2022

Besides social network analysis, the Link-Prediction (LP) problem has useful applications in information retrieval, bioinformatics, telecommunications, microbiology, and e-commerce as a forecast of future links in a given context to find what possible connections are based on a local and global statistical analysis of the given graph data. However, in Academic Social Networks (ASNs), the LP issue has recently attracted a lot of attention in academia and called for a variety of link prediction techniques to predict co-authorship among researchers and to examine the rich structural and associated data. As a result, this study investigates the problem of LP in ASNs to forecast the upcoming co-authorships among researchers. In a systematic approach, this review presents, analyses, and compares the primary taxonomies of topological-based, content-based, and hybrid-based approaches, which are used for computing similar scores for each pair of unconnected nodes. Then, this study ends with findings on challenges and open problems for the community to work on for further development of the LP problem of scholarly social networks.

View PDFchevron_right
Link prediction of scientific collaboration networks based on information retrieval
Dmitry Lande

World Wide Web, 2020

Link prediction plays an important role in scientific collaboration networks, and can favourably affect the organization of international scientific projects. In this paper, a metapath computed prediction (MPCP) algorithm for link prediction among scientists and publications is presented. The MPCP algorithm is based on a heterogeneous information network model composed of authors and keywords in articles retrieved from the Web of Science database.

View PDFchevron_right
Link prediction in co-authorship networks based on hybrid content similarity metric
Phạm Chuẩn

Applied Intelligence, 2017

Link prediction in online social networks is used to determine new interactions among its members which are likely to occur in the future. Link prediction in the coauthorship network has been regarded as one of the main targets in link prediction researches so far. Researchers have focused on analyzing and proposing solutions to give efficient recommendation for authors who can work together in a science project. In order to give precise prediction of

View PDFchevron_right
Analysis and Prediction in Academic Collaboration Networks - BS Thesis (2008)
Dicle Ozturk

The primary aim of the project is to learn social network analysis, link analysis and prediction in these types of networks where the nodes are usually people and the links are the relationships among them depending on the context of the field. Research in this area involves machine learning and data mining applications, as well. Therefore, both learning and using these applications, in addition to constructing a background in social network for further research and application are the core goals of the project.

View PDFchevron_right
Supervised Machine Learning Applied to Link Prediction in Bipartite Social Networks
Rushed Kanawati

2010 International Conference on Advances in Social Networks Analysis and Mining, 2010

This work copes with the problem of link prediction in large-scale two-mode social networks. Two variations of the link prediction tasks are studied: predicting links in a bipartite graph and predicting links in a unimodal graph obtained by the projection of a bipartite graph over one of its node sets. For both tasks, we show in an empirical way, that taking into account the bipartite nature of the graph can enhance substantially the performances of prediction models we learn. This is achieved by introducing new variations of topological atttributes to measure the likelihood of two nodes to be connected. Our approach, for both tasks, consists in expressing the link prediction problem as a two class discrimination problem. Classical supervised machine learning approaches can then be applied in order to learn prediction models. Experimental validation of the proposed approach is carried out on two real data sets: a co-authoring network extracted from the DBLP bibliographical database and bipartite graph 8-years history of transactions on an on-line music e-commerce site.

View PDFchevron_right

Related topics

  • Information Systems
  • Computer Science
  • Distributed Computing

    • Cited by

    A novel TCNN–Bi‐LSTM deep learning model for predicting sentiments of tweets about COVID‐19 vaccines
    Serpil ASLAN

    Concurrency and Computation: Practice and Experience

    Many researchers in various disciplines have focused on extracting meaningful information from social media platforms in recent years. Identification of behaviors and emotions from user posts is examined under the heading of sentiment analysis (SA) studies using the natural language processing (NLP) techniques. In this study, a novel TCNN-Bi-LSTM model using the two-stage convolutional neural network (TCNN) and bidirectional long short-term memory (Bi-LSTM) architectures was proposed. While TCNN layers enable the extraction of strong local features, the output of these layers feeds the Bi-LSTM model that remembers forward-looking information and capture long-term dependencies. In this study, first, preprocessing steps were applied to the raw dataset. Thus, strong features were extracted from the obtained quality dataset using the FastText word embedding technique that pre-trained with location-based and sub-word information features. The experimental results of the proposed method are promising compared to the baseline deep learning and machine learning models. Also, experimental results show that while the FastText data embedding technique achieves the best performance compared to other word embedding techniques in all deep learning classification models, it has not had the same outstanding success in machine learning models. This study aims to investigate the sentiments of tweets about the COVID-19 vaccines and comments on these tweets among Twitter users by using the power of Twitter data. A new dataset collected from Twitter was constructed to be used in experimental results. This study will facilitate detecting inappropriate, incomplete, and erroneous information about vaccination. The results of this study will enable society to broaden its perspective on the administered vaccines. It can also assist the government and healthcare agencies in planning and implementing the vaccination's promotion on time to achieve the herd immunity provided by the vaccination.

    View PDFchevron_right
    580 California St., Suite 400
    San Francisco, CA, 94104
    © 2025 Academia. All rights reserved