Link Prediction in Online Social Networks Using Group Information

2019 IEEE International Conference on Big Data (Big Data)

Due to its universal applications in the domain of social network analysis, e-commerce, and recommendation systems, the task of link prediction has received enormous attention from the data mining and machine learning communities over the last decade. In its original setting, the task only predicts whether a pair of entities who are not connected at present time will form a connection in future. However, in real-life an entity sometimes join a group (or a community), thus making a connection with the group (or the community), instead of connecting with an individual. Existing solutions to link prediction are inadequate for solving this prediction task. To overcome this challenge, in this work we propose a novel problem named group link prediction which focuses on evaluating the likelihood for a candidate to become a member of a group at a given time. The problem has potential applications such as friendship or group suggestions on Facebook or other social networks, as well as co-authorship suggestion, or group email recommendations. To solve the problem, we propose a Long Short-term Memory based model that inputs the embedding vectors of the group and outputs the conditional probability distributions for the candidates. We also introduce a composite long short-term memory model that integrates keyword information. Experimental results on realworld data sets validate the superiority of our proposed model in comparison to various baseline methods.

International Journal of Advanced Computer Science and Applications, 2020

This work extends my previous work on link prediction in Social Networks. In this research, I used two additional datasets, Twitter dataset and Facebook Social Circles Dataset and I ran link prediction methods on these datasets. In my previous work, I performed experiment on the Facebook dataset and proposed two new link prediction methods: Neighbors Connectivity and Common Neighbors of Neighbors (CNN). As in my previous work, in this work, I ran the link prediction methods for several training and testing sizes. Results showed that For Facebook dataset, random had the highest precision, followed by Neighbors Connectivity, then Preferential Attachment, followed by Jaccard/CC, Adamic-Adar, finally CNN. For Twitter dataset, random achieved the highest precision. Preferential Attachment achieved the next highest precision, and Adamic-Adar achieved the least precision. For Facebook Social Circles dataset, Preferential-Attachment achieved the highest precision of 1.08891 followed by random for a training and testing sizes of (1535, 2504) respectively. That is said with slight variation on the orders depending on the training and testing size. The low precision values achieved with Facebook and Twitter datasets are due to the graph types which are sparse as indicated in the datasets websites which confirms Kleinberg finding.

International Journal of Advanced Computer Science and Applications, 2018

The unparalleled accomplishment of social networking sites, such as Facebook, LinkedIn and Twitter has modernized and transformed the way people communicate to each other. Nowadays, a huge amount of information is being shared by online users through these social networking sites. Various online friendship sites such as Facebook and Orkut, allow online friends to share their thoughts or opinions, comment on others' timeline or photos, and most importantly, meet new online friends who were known to them before. However, the question remains as to how to quickly propagate one's online network by including more and more new friends. For this, one of the easy methods used is list of 'Suggested Friends' provided by these online social networking sites. For suggestion of friends, prediction of links for each online user is needed to be made based on studying the structural properties of the network. Link prediction is one of the key research directions in social network analysis which has attracted much attention in recent years. This paper discusses about a novel efficient link prediction technique LinkGyp and many other commonly used existing prediction techniques for suggestion of friends to online users of a social network and also carries out experimental evaluations to make a comparative analysis among each technique. Our results on three real social network datasets show that the novel LinkGyp link prediction technique yields more accurate results than several existing link prediction techniques.

One of the major fields of study in the analysis and mining of social networks is the study of social network patterns and development. Link prediction has attracted extensive attention from the scientist in the network science. Despite incredible endeavors to create modern prediction algorithms that can give way better prediction precision in social network topologies that are extricated from complex datasets approximately clients, their exercises, and connections. But most of the recent approaches have not been broadly evaluated. This paper focuses on the missing link prediction between nodes in a systematic way by formalizing and developing approaches about future interactions that can be extracted from network topology for analyzing the proximity measures of nodes, links, and their attributes in a network. The main assumption of this paper is that the missing links must have opposite features concerning the original links. It turns out that feature engineering via measured the node or edge attributes and network structures based provided the best characterization in this sense. Some of network topology-based prediction methods are Common Neighbors, Sørensen Index, Hub Depressed Index, Adamic Index, Salton's cosine formula, spectral clustering, etc. Experimental results using AUC and ROC proved that based on Network topology and previous information may be more or less difficult to accurately predicted future connections so, there is no single clear winner among similarity metrics.

SDM’06: Workshop on Link Analysis, Counter-terrorism and Security, 2006

Social network analysis has attracted much attention in recent years. Link prediction is a key research direction within this area. In this paper, we study link prediction as a supervised learning task. Along the way, we identify a set of features that are key to the performance under the supervised learning setup. The identified features are very easy to compute, and at the same time surprisingly effective in solving the link prediction problem. We also explain the effectiveness of the features from their class density distribution. Then ...

Information Sciences, 2019

In recent years, most link prediction algorithms have focused on node similarity owing to the associated low computational complexity and promising accuracy. In addition to the classical CN-based indexes, some methods are based on network features, such as community structure, information dissemination, and intermediary influence probability, which are used for link prediction. Although these methods provide new insight into the problem and achieve improvements in certain respects, they also have some limitations. For example, it is difficult to predict target links if the number of interconnections between communities is small. However, most studies aim at achieving higher link prediction accuracy even though a network obtained by these methods is not optimized for information spread. Therefore, we propose a community-based link prediction method using an information diffusion algorithm (CLP-ID) to predict the missing links. First, we present a community detection algorithm that divides the network into clusters. Then, a novel algorithm based on information diffusion and community structure is proposed to predict target links. Finally, we conduct experiments on real-world networks to validate the performance of the proposed algorithm and compare it with state-of-the-art algorithms. Statistical tests demonstrate that the proposed method significantly differs from state-of-the-art algorithms.

International Journal of Modeling and Optimization, 2014

Link prediction is a very well studied problem as it has applications in many different areas. Many algorithms have been presented in the literature for the Link prediction problem. The general for of the problem is that given the topology of graph G at a certain time t, we need to predict the topology of the graph G at time t' where t' > t assuming that the number of nodes does not change. The techniques used for Link prediction are categorized into three types: Nodes based techniques, Link based techniques and Path based techniques. Then there are other techniques that use meta-approaches such as.....which are based on the basic techniques. In this paper we conduct a survey of all the existing Link Prediction techniques to the best of our knowledge and perform an experimental comparison of these techniques. We use real social network data for the testing.

Lecture Notes in Social Networks, 2014

Traditional link prediction techniques primarily focus on the effect of potential linkages on the local network neighborhood or the paths between nodes. In this article, we study both supervised and unsupervised link prediction in networks where instances can simultaneously belong to multiple communities, engendering different types of collaborations. Links in these networks arise from heterogeneous causes, limiting the performance of predictors that treat all links homogeneously. To solve this problem, we introduce a new supervised link prediction framework, Link Prediction using Social Features (LPSF), which incorporates a reweighting scheme for the network based on nodes' features extracted from patterns of prominent interactions across the network. Experiments on coauthorship networks demonstrate that the choice for measuring link weights can be critical for the link prediction task. Our proposed reweighting method in LPSF better expresses the intrinsic relationship between nodes and improves prediction accuracy for supervised link prediction techniques. We also compare the unsupervised performance of the individual features used within LPSF with two new diffusion-based methods: LPDP (Link Prediction using Diffusion Process) and LPDM (Link Prediction using Diffusion Maps). Experiments demonstrate that LPDP is able to identify similar node pairs, even far away ones, that are connected by weak ties in the coauthorship network using the diffusion process; however, reweighting the network has little impact on prediction performance.

The Third IEEE International Conference on Social Computing (SocialCom2011), MIT, Boston, USA

"Online social networking sites have become increas-ingly popular over the last few years. As a result, new interdisci-plinary research directions have emerged in which social network analysis methods are applied to networks containing hundreds millions of users. Unfortunately, links between individuals may be missing either due to imperfect acquirement processes or because they are not yet reflected in the online network (i.e., friends in real-world did not form a virtual connection.) Existing link prediction techniques lack the scalability required for full application on a continuously growing social network. The primary bottleneck in link prediction techniques is ex-tracting structural features required for classifying links. In this paper we propose a set of simple, easy-to-compute structural features, that can be analyzed to identify missing links. We show that by using simple structural features, a machine learning classifier can successfully identify missing links, even when applied to a hard problem of classifying links between individuals with at least one common friend. A new friends measure that we developed is shown to be a good predictor for missing links. An evaluation experiment was performed on five large Social Networks datasets: Facebook, Flickr, YouTube, Academia and TheMarker. Our methods can provide social network site operators with the capability of helping users to find known, offline contacts and to discover new friends online. They may also be used for exposing hidden links in an online social network."

Proceedings of the 30th Annual ACM Symposium on Applied Computing - SAC '15, 2015

Link prediction in online social networks is useful in numerous applications, mainly for recommendation. Recently, different approaches have considered friendship groups information for increasing the link prediction accuracy. Nevertheless, these approaches do not consider the different roles that common neighbors may play in the different overlapping groups that they belong to. In this paper, we propose a new approach that uses overlapping groups structural information for building a naïve Bayes model. From this proposal, we show three different measures derived from the common neighbors. We perform experiments for both unsupervised and supervised link prediction strategies considering the link imbalance problem. We compare sixteen measures in four well-known online social networks: Flickr, LiveJournal, Orkut and Youtube. Results show that our proposals help to improve the link prediction accuracy.