The ML-model for multi-layer social networks

2022

Graph representations for real-world social networks in the past have missed two important elements: the multiplexity of connections as well as representing time. To this end, in this paper, we present a new dynamic heterogeneous graph representation for social networks which includes time in every single component of the graph, i.e., nodes and edges, each of different types that captures heterogeneity. We illustrate the power of this representation by presenting four timedependent queries and deep learning problems that cannot easily be handled in conventional homogeneous graph representations commonly used. As a proof of concept we present a detailed representation of a new social media platform (Steemit), which we use to illustrate both the dynamic querying capability as well as prediction tasks using graph neural networks (GNNs). The results illustrate the power of the dynamic heterogeneous graph representation to model social networks. Given that this is a relatively understudied area we also illustrate opportunities for future work in query optimization as well as new dynamic prediction tasks on heterogeneous graph structures.

In the last few years, there is a rapid growth of web and various social network sites which have enabled us to easily interconnect people all over the world in a shared platform. A social network is a social structure comprising individuals or organizations which hold dynamic ties between them. Social network can be visualized in terms of connected graph where individuals are represented by vertices or nodes and connections between individuals are represented by link or edges. The tendency of people based on their preferences, choices, likes or dislikes are associated with each other in a shared platform, which forms a virtual cluster or community. In this paper we generate a graph of communication network based on real life data collected from a social network site-Twitter. Several community detection algorithms are in place and our intention is to make a comparative study of these existing algorithms over our graph and detect the communities which cannot be viewed by mere observa...

With hundreds of millions of users worldwide, social networks provide incredible opportunities for social connection, learning, political and social change, and individual entertainment and enhancement in a wide variety of forms. In light of these notable outcomes, understanding information diffusion over online social networks is a critical research goal. Because many social interactions currently take place in online networks, we now have have access to unprecedented amounts of information about social interaction. Prior to the advent of such online networks, investigations about social behavior required resource-intensive activities such as random trials, surveys, and manual data collection to gather even small data sets. Now, massive amounts of information about social networks and social interactions are recorded. This wealth of data can allow us to study social interactions on a scale and at a level of detail that has never before been possible. We present an integrated approach to information diffusion in online social networks focusing on three key problems: (1) Querying and analysis of online social network datasets; (2) Modeling and analysis of social networks; and (3) Analysis of social media and social interactions in the contemporary media environment. The overarching goals are to generate a greater understanding of social interactions in online networks through data analysis, to develop reliable and scalable models that can predict outcomes of these social processes, and ultimately to create applications that can shape the outcome of these processes. We start by developing and refining models of information diffusion based on real-world data sets. We next address the problem of finding influential users in this data-driven framework. It is equally important to identify techniques that can slow or prevent the spread of misinformation, and hence algorithms are explored to address this question. A third interest is the process by which a social group forms opinions about an idea or product, and we therefore describe preliminary approaches to create models that accurately capture the opinion formation process in online social networks. While questions relating to the propagation of a single news item or idea are important, these information campaigns do not exist in isolation. Therefore, our proposed approach also addresses the interplay of the many information diffusion processes that take place simultaneously in a network and the relative importance of different topics or trends over multiple spatial and temporal resolutions.

Scientific Reports, 2014

Inspired by the analysis of several empirical online social networks, we propose a simple reaction-diffusion-like coevolving model, in which individuals are activated to create links based on their states, influenced by local dynamics and their own intention. It is shown that the model can reproduce the remarkable properties observed in empirical online social networks; in particular, the assortative coefficients are neutral or negative, and the power law exponents c are smaller than 2. Moreover, we demonstrate that, under appropriate conditions, the model network naturally makes transition(s) from assortative to disassortative, and from sparse to dense in their characteristics. The model is useful in understanding the formation and evolution of online social networks. M assive websites-Facebook, Twitter, MySpace, LinkedIn, Flickr, Orkut, Google1, Weaklink, just to name a few-are booming in the past few years, where millions of users and their interactions naturally form the so called online social networks (OSNs) 1-3. For OSNs, one important characteristic is the strong interplay between the user behaviour and the network topology 4. On the one hand, the user behaviour is affected by the topology-dependent information flowing in the networks 5-8 ; on the other hand, the network topology continually evolves as a natural consequence of network dynamics 8-10. Due to this feature, OSNs exhibit certain correlation patterns during evolution, such as the highly skewed degree distributions 11-13 , the generalized Gibrat's Law 14 , assortativity/disassortativity 11,12 , etc, which are of great importance for us to understand the possible generic laws governing the organization and evolution in networked systems 15. Recently, two interesting phenomena in OSNs have attracted much attention. The first one is related to the assortativity/disassortativity property of the network, which is an important structural measure characterizing the degree correlation between pairwise nodes. Mathematically, the assortative coefficient can be defined as the Pearson correlation coefficient averaged for all pairs of adjacent nodes in the network. As shown in Table I, it is reported that some OSNs (e.g., Twitter and Cyworld) show negative or neutral assortative coefficients 11-13,16-19 , and some OSNs, such as Weaklink 11 and Google1 (G1) 12 , even convert from being assortative to being disassortative during evolution. These findings challenge our traditional knowledge 20,21 that biological and technical networks (e.g., financial networks 22) are disassortative, while social networks (e.g., acquaintance networks 23) are assortative. Secondly, the scale-free property is of great importance for a network, which can be characterized by a power law exponent c as in p(k) , k 2c , where k and p(k) are node degree and the distribution of degree, respectively. Under the thermodynamical limit, i.e., the network size N R ', the mean degree of a scale-free network will diverge when c # 2. Therefore, c 5 2 is an important boundary, and scale-free networks can be classified into dense (c # 2) and sparse (c. 2) accordingly. Previously, many scale-free networks are found to be sparse 24. However, as shown in Table I, some large OSNs, e.g., YouTube (YT), Digg, and LiveJournal (LJ), turn out to be dense scale-free networks with c , 2 13,19,25. In Table I, the basic statistical properties for 14 popular OSNs are listed. It is found that these OSNs basically share common properties observed in real world networks, such as power-law distribution of degrees, large

2013

Microblogs such as Twitter support a rich variety of user interactions using hashtags, urls, retweets and mentions. Microblogs are an exemplar of a hybrid network; there is an explicit network of followers, as well as an implicit network of users who retweet other users, and users who mention other users. These networks are important proxies for influence. In this paper, we develop a comprehensive behavioral model of an individual user and her interactions in the hybrid network. We choose a focal user and predict those users who will be influenced by her, and will retweet and/or mention the focal user, in the near future. We define a potential function, based on a hybrid network, which reflects the likelihood of a candidate user being influenced by, and having a specific type of link to, a focal user, in the future. We show that the potential function based prediction model converges to the Bonacich centrality metric. We develop a fast unsupervised solution which approximates the future hybrid network and the future Bonacich potential. We perform an extensive evaluation over a microblog network and a stream of tweets from Twitter. Our solution outperforms several baseline methods including ones based on singular value decomposition (SVD) and a supervised Ranking SVM.

Graph Theoretic Approaches for Analyzing Large-Scale Social Networks

With the advent of online social media, it is possible to collect large information and extract information. One of the powerful roles that networks play is to bridge gap between the local and the global perspectives. For example, social network analysis could be used to offer explanations for how simple processes at the level of individual nodes and links can have a global effect. Social networks like Twitter, Facebook, LinkedIn are very large in size with millions of vertices and billions of edges. To collect meaningful information from these densely connected graphs and huge volume of data, it is important to find proper topology of the network as well as conduct analysis based on different network parameters. The main objective of this work is to study the network parameters commonly used to explain social structures. We extract data from the three real-time Facebook accounts using the Netvizz application, analyze and evaluate their network parameters on some widely recognized graph topology using Gephi, a free and open source software.

A social network is an abstract concept consisting of the set of people with relationships linking pairs of humans. A new multidimensional model, which covers three main dimensions: relation layers, time windows and groups, is proposed in the paper. The dimensions have a common set of nodes, typically, corresponding to human beings Layers, in turn, reflect various relation types coming from different user activities gathered in computer systems. The time dimension corresponds to temporal variability of the social network. Social groups are extracted by means of clustering methods and join people close each other. An atom component of the multidimensional social network are views -small social sub-networks, which are in the intersection of all dimensions. A view describes the state of one social group, linked by only one type of relationships (one layer), derived from within only one time period. The multidimensional model of the social network is similar to the general concept of data warehouses, in which a fact corresponds to a view. Aggregation possibilities and usage of the model is also discussed in the paper.

2020

We consider data with multiple observations or reports on a network in the case when these networks themselves are connected through some form of network ties. We could take the example of a cognitive social structure where there is another type of tie connecting the actors that provide the reports; or the study of interpersonal spillover effects from one cultural domain to another facilitated by the social ties. Another example is when the individual semantic structures are represented as semantic networks of a group of actors and connected through these actors' social ties to constitute knowledge of a social group. How to jointly represent the two types of networks is not trivial as the layers and not the nodes of the layers of the reported networks are coupled through a network on the reports. We propose to transform the different multiple networks using line graphs, where actors are affiliated with ties represented as nodes, and represent the totality of the different types ...

In the last years, complex networks have become an increasingly relevant research topic due to the large number of fields of application. Particularly, complex networks are specially significant in the area of modern Online Social Networks (OSNs). OSNs are actually a challenge for complex network analysis, as they present some characteristics that hinder topology processing. Concretely, social-networks volume is exceedingly big, as they have a high number of nodes and links. One of the most popular and influential OSN is Twitter. This article proves that twitter is a complex network computing an estimated value of luster coefficient, power law, and average path length.

2017

In the last decade, online social networks showed enormous growth. With the rise of these networks and the consequent availability of wealth social network data, Social Network Analysis (SNA) led researchers to get the opportunity to access, analyse and mine the social behaviour of millions of people, explore the way they communicate and exchange information. Despite the growing interest in analysing social networks, there are some challenges and implications accompanying the analysis and mining of these networks. For example, dealing with large-scale and evolving networks is not yet an easy task and still requires a new mining solution. In addition, finding communities within these networks is a challenging task and could open opportunities to see how people behave in groups on a large scale. Also, the challenge of validating and optimizing communities without knowing in advance the structure of the network due to the lack of ground truth is yet another challenging barrier for vali...