Evolving Networks and Social Network Analysis Methods and Techniques

Evolving networks by definition are networks that change as a function of time. They are a natural extension of network science since almost all real-world networks evolve over time, either by adding or by removing nodes or links over time: elementary actor-level network measures like network centrality change as a function of time, popularity and influence of individuals grow or fade depending on processes, and events occur in networks during time intervals. Other problems such as network-level statistics computation, link prediction, community detection, and visualization gain additional research importance when applied to dynamic online social networks (OSNs). Due to their temporal dimension, rapid growth of users, velocity of changes in networks, and amount of data that these OSNs generate, effective and efficient methods and techniques for small static networks are now required to scale and deal with the temporal dimension in case of streaming settings. This chapter reviews the state of the art in selected aspects of evolving social networks presenting open research challenges related to OSNs. The challenges suggest that significant further research is required in evolving social networks, i.e., existent methods, techniques, and algorithms must be rethought and designed toward incremental and dynamic versions that allow the efficient analysis of evolving networks

SAMPLING AND CHARACTERIZING EVOLVING COMMUNITIES IN SOCIAL NETWORKS

One of the most important structures in social networks is communities. Understanding communities is useful in many applications, such as suggesting a friend for a user in an online friendship network, recommending a product for a user in an e-commerce network, etc. However, before studying anything about communities, researchers first need to collect appropriate data. Getting complete access to the data for community studies is unrealistic in most cases. In this work, we address the problem of crawling networks to identify community structure. Firstly, we present a network sampling technique to crawl the community structure of dynamic networks when there is a limitation on the number of nodes that can be queried. The process begins by obtaining a sample for the first-time step. In subsequent time steps, the crawling process is guided by community structure discoveries made in the past. Experiments conducted on the proposed approach and certain baseline techniques reveal the proposed approach has at least a 35% performance increase in cases when the total query budget is fixed over the entire period and at least an 8% increase in cases when the query budget is fixed per time step. Secondly, we propose a sampling technique to sample communities in node attributed edge streams when there is a limit on the maximum number of nodes that can be stored. The process learns if the nodal information can characterize communities. The nodal information is leveraged with the structural information to generate representative communities. If the nodal information does not characterize communities, only structural information is considered in assigning nodes to communities. The proposed approach provides a performance improvement of up to about 5 times that of baselines. Finally, we investigate factors that characterize the evolution of communities with respect to the number of active users. We perform this investigation on the Reddit social media platform. We begin by first analyzing individual conversations of one community and sees how that generalizes to other communities. The first community studied is Reddit’s changemyview. The changemyview community, in addition to its rich data source, has an interesting property where members whose view are changed award points to users that successfully changed their minds. From the changemyview community, we observe that the linguistic style and interactions of members of the community can significantly differentiate susceptible and non-susceptible users. Next, we examine other communities (subreddits), and investigate how the user behaviors observed from changemyview relate to patterns of community evolution. We learn that the linguistic style and interactions of members in a community can also significantly differentiate the different parts of the evolution of the community with respect to number of active users