Adaptive algorithms for detecting community structure in dynamic social networks

Abstract

Social networks exhibit a very special property: community structure. Understanding the network community structure is of great advantages. It not only provides helpful information in developing more social-aware strategies for social network problems but also promises a wide range of applications enabled by mobile networking, such as routings in Mobile Ad Hoc Networks (MANETs) and worm containments in cellular networks. Unfortunately, understanding this structure is very challenging, especially in dynamic social networks where social activities and interactions are evolving rapidly. Can we quickly and efficiently identify the network community structure? Can we adaptively update the network structure based on previously known information instead of recomputing from scratch?In this paper, we present Quick Community Adaptation (QCA), an adaptive modularity-based method for identifying and tracing community structure of dynamic online social networks. Our approach has not only the power of quickly and efficiently updating network communities, through a series of changes, by only using the structures identified from previous network snapshots, but also the ability of tracing the evolution of community structure over time. To illustrate the effectiveness of our algorithm, we extensively test QCA on real-world dynamic social networks including ENRON email network, arXiv e-print citation network and Facebook network. Finally, we demonstrate the bright applicability of our algorithm via a realistic application on routing strategies in MANETs. The comparative results reveal that social-aware routing strategies employing QCA as a community detection core outperform current available methods.

Detecting Community Structure in Dynamic Social Networks Using the Concept of Leadership

“…Community memberships of affected nodes will be determined simultaneously, proportional to structural changes in the network. Similar to that, Nguyen et al [12] proposed another algorithm to find modular communities in each snapshot. Although their method is faster than the method which clusters each snapshot independently [4], ), running their method for a long time on a dynamic network will end up in poor quality results.…”

“…However, in reality, due to the dynamic nature of the social networks, they continuously evolve. These changes could be joining (leaving) actors to (from) the network and establishing new connections or destroying the existing ones [12]. This makes a highly dynamic network which witnesses a wide variety of changes.…”

A Method of Developing a Generic Social Network

“…Community evolution mining in dynamic social networks was implemented in [20], where the events detected by the framework was supplemented by extraction and investigation of the topics discovered for each community. Michele [21] showed graph evolution rules which helped in analyzing the evolution of large networks and could be used to predict the future creation of links among nodes, whereas adaptive algorithms for detecting community structure in dynamic social networks were implemented [22] and demonstrated bright applicability of algorithms. Multi-level multi-theoretical model which gave a theoretical framework to explain the evolution of communication networks within teams was shown in [23], and enabled researchers to analyze dynamic network patterns of virtual teams.…”

Adaptive Online Estimation of Temporal Connectivity in Dynamic Wireless Networks

“…Especially, knowing how reachable a node is from every other node in the network provides a wealth of information regarding the spread of information [5], the best route to send a message from source to destination [16], [18], and relationship between participants in a large crowd [17]. This sets the context for enabling self-* applications, in which nodes can tune parameters such as the message sending rate in data networks, or the extent of interaction between participants at a conference.…”