Time-evolving Social Networks Research Articles

STGSN — A Spatial–Temporal Graph Neural Network framework for time-evolving social networks

Social Network Analysis (SNA) has been a popular field of research since the early 1990s. Law enforcement agencies have been utilizing it as a tool for intelligence gathering and criminal investigation for decades. However, the graph nature of social networks makes it highly restricted to intelligence analysis tasks, such as role prediction (node classification), social relation inference (link prediction), and criminal group discovery (community detection), etc. In the past few years, many studies have focused on Graph Neural Network (GNN), which utilizes deep learning methods to solve graph-related problems. However, we have rarely seen GNNs tackle time-evolving social network problems, especially in the criminology field. The existing studies have commonly over-looked the temporal-evolution characteristics of social networks. In this paper, we propose a graph neural network framework, namely Spatial-Temporal Graph Social Network (STGSN), which models social networks from both spatial and temporal perspectives. Using a novel approach, we leverage the temporal attention mechanism to capture social networks’ temporal features. We design a method analyzing temporal attention distribution to improve the interpretation ability of our method. In the end, we conduct extensive experiments on six public datasets to prove our methods’ effectiveness.

Tensor decomposition for analysing time-evolving social networks: an overview

Social networks are becoming larger and more complex as new ways of collecting social interaction data arise (namely from online social networks, mobile devices sensors, ...). These networks are often large-scale and of high dimensionality. Therefore, dealing with such networks became a challenging task. An intuitive way to deal with this complexity is to resort to tensors. In this context, the application of tensor decomposition has proven its usefulness in modelling and mining these networks: it has not only been applied for exploratory analysis (thus allowing the discovery of interaction patterns), but also for more demanding and elaborated tasks such as community detection and link prediction. In this work, we provide an overview of the methods based on tensor decomposition for the purpose of analysing time-evolving social networks from various perspectives: from community detection, link prediction and anomaly/event detection to network summarization and visualization. In more detail, we discuss the ideas exploited to carry out each social network analysis task as well as its limitations in order to give a complete coverage of the topic.

Time-Evolving Social Network Generator Based on Modularity: TESNG-M

Dynamic social networking has always been the focus of the social network research, and a large number of effective community detection algorithms have been developed. However, as the real social networks are difficult to access, it is also difficult to evaluate the effectiveness of the community detection algorithms on dynamic social networks. Existing dynamic social network generators only focus on edge or node changes, whereas the quality of the community structure (modularity) is not considered. We propose a time-evolving social network generator based on modularity (TESNG-M). In TESNG-M, according to the community partition of the original network, the evolutionary behavior is simulated by adding or deleting nodes and flipping edges so that a static social network with a specified modularity will be generated. By repeating the static generation process, we obtain a dynamic social network with a specified partition and modularity at each time step. Thus, the network generated by TESNG-M can effectively simulate a real dynamic social network and be used for community detection. Furthermore, the specified modularity of static synthetic networks and the dynamic modularity of dynamic synthetic networks could be regarded as the performance baseline of community detection algorithms in static and dynamic social networks, respectively.

Modeling and Predicting Community Structure Changes in Time-Evolving Social Networks

As time evolves, communities in a social network may undergo various changes known as critical events. For instance, a community can either split into several other communities, expand into a larger community, shrink to a smaller community, remain stable or merge into another community. Prediction of critical events has attracted increasing attention in the recent literature. Learning the evolution of communities over time is a key step towards predicting the critical events the communities may undergo. This is an important and difficult issue in the study of social networks. In the work to date, there is a lack of formal approaches for modeling and predicting critical events over time. This motivates our effort to design a new statistical method for event prediction in order to make better use of histories of past changes. To this end, this paper proposes a sliding window analysis from which we develop a model that simultaneously exploits an autoregressive model and survival analysis techniques. The autoregressive model is employed here to simulate the evolution of the community structure, whereas the survival analysis techniques allow the prediction of future changes the community may undergo.

Zealotry effects on opinion dynamics in the adaptive voter model.

The adaptive voter model has been widely studied as a conceptual model for opinion formation processes on time-evolving social networks. Past studies on the effect of zealots, i.e., nodes aiming to spread their fixed opinion throughout the system, only considered the voter model on a static network. Here we extend the study of zealotry to the case of an adaptive network topology co-evolving with the state of the nodes and investigate opinion spreading induced by zealots depending on their initial density and connectedness. Numerical simulations reveal that below the fragmentation threshold a low density of zealots is sufficient to spread their opinion to the whole network. Beyond the transition point, zealots must exhibit an increased degree as compared to ordinary nodes for an efficient spreading of their opinion. We verify the numerical findings using a mean-field approximation of the model yielding a low-dimensional set of coupled ordinary differential equations. Our results imply that the spreading of the zealots' opinion in the adaptive voter model is strongly dependent on the link rewiring probability and the average degree of normal nodes in comparison with that of the zealots. In order to avoid a complete dominance of the zealots' opinion, there are two possible strategies for the remaining nodes: adjusting the probability of rewiring and/or the number of connections with other nodes, respectively.

Discrete temporal models of social networks

We propose a family of statistical models for social network evolution over time, which represents an extension of Exponential Random Graph Models (ERGMs). Many of the methods for ERGMs are readily adapted for these models, including maximum likelihood estimation algorithms. We discuss models of this type and their properties, and give examples, as well as a demonstration of their use for hypothesis testing and classification. We believe our temporal ERG models represent a useful new framework for modeling time-evolving social networks, and rewiring networks from other domains such as gene regulation circuitry, and communication networks.