Community Evolution In Social Networks Research Articles

MODEC — Modeling and Detecting Evolutions of Communities

Social network analysis encompasses the study of networked data and examines questions related to structures and patterns that can lead to the understanding of the data and the intrinsic relationships, such as identifying influential nodes, recognizing critical paths, predicting unobserved relationships, discovering communities, etc. All of these analyses, germane to a variety of application domains, are typically done on static information networks; that is, a fixed snapshot of the information network. Yet, a social network changes and understanding the evolution of the network and detecting these changes in the underlying structures is paramount for a multitude of applications. Looking at networks as fixed snapshots misses the opportunity to capture the evolutionary patterns. In this paper, we present a framework for modeling community evolution in social networks by tracking of events related to the life cycle of a community. We illustrate the capabilities of our framework by applying it to real datasets and validate the results using topics extracted from the tracked communities.

Evolution of communities in dynamic social networks: An efficient map-based approach

The expanded domain of expert system applications has risen the impact of modeling and analysis of community evolution in social networks as an important part of the decision-making process. Social networks are time-variant systems, evolving through entities joining or leaving networks and establishing or terminating relationships. In this article, we study evolution of social networks at the level of community structure, by tracking different transformations of communities over time. Upon experimentation, we observed that a considerable portion of community evolution is partial events such as partial merge. Therefore, we define a broader set of community evolution to include partial events. Furthermore, we introduce ICEM, a novel method for Identification of Community Evolution by Mapping. ICEM determines community evolution by tracking community members, implemented with a hash-map. ICEM maps each member to a (t, c) pair, specifying it is last observed in time window t and community c. We evaluated our proposed approach with seventeen publicly available social network datasets and compared its performance against other well-known methods in the literature. Our experimental results indicated the performance superiority of our proposed solution. Additionally, we conducted separate comprehensive experiments using three community detection algorithms to highlight the effect of choosing different community discovery methods on community evolution results.

Tracking community evolution in social networks: A survey

This paper presents a survey of previous studies done on the problem of tracking community evolution over time in dynamic social networks. This problem is of crucial importance in the field of social network analysis. The goal of our paper is to classify existing methods dealing with the issue. We propose a classification of various methods for tracking community evolution in dynamic social networks into four main approaches using as a criterion the functioning principle: the first one is based on independent successive static detection and matching; the second is based on dependent successive static detection; the third is based on simultaneous study of all stages of community evolution; finally, the fourth and last one concerns methods working directly on temporal networks. Our paper starts by giving basic concepts about social networks, community structure and strategies for evaluating community detection methods. Then, it describes the different approaches, and exposes the strengths as well as the weaknesses of each.

Predicting Community Evolution in Social Networks

Nowadays, sustained development of different social media can be observed worldwide. One of the relevant research domains intensively explored recently is analysis of social communities existing in social media as well as prediction of their future evolution taking into account collected historical evolution chains. These evolution chains proposed in the paper contain group states in the previous time frames and its historical transitions that were identified using one out of two methods: Stable Group Changes Identification (SGCI) and Group Evolution Discovery (GED). Based on the observed evolution chains of various length, structural network features are extracted, validated and selected as well as used to learn classification models. The experimental studies were performed on three real datasets with different profile: DBLP, Facebook and Polish blogosphere. The process of group prediction was analysed with respect to different classifiers as well as various descriptive feature sets extracted from evolution chains of different length. The results revealed that, in general, the longer evolution chains the better predictive abilities of the classification models. However, chains of length 3 to 7 enabled the GED-based method to almost reach its maximum possible prediction quality. For SGCI, this value was at the level of 3–5 last periods.

Community Evolution Mining in Dynamic Social Networks

Data that encompasses relationships is represented by a graph of interconnected nodes. Social network analysis is the study of such graphs which examines questions related to structures and patterns that can lead to the understanding of the data and predicting the trends of social networks. Static analysis, where the time of interaction is not considered (i.e., the network is frozen in time), misses the opportunity to capture the evolutionary patterns in dynamic networks. Specifically, detecting the community evolutions, the community structures that changes in time, provides insight into the underlying behaviour of the network. Recently, a number of researchers have started focusing on identifying critical events that characterize the evolution of communities in dynamic scenarios. In this paper, we present a framework for modeling and detecting community evolution in social networks, where a series of significant events is defined for each community. A community matching algorithm is also proposed to effciently identify and track similar communities over time. We also define the concept of meta community which is a series of similar communities captured in different timeframes and detected by our matching algorithm. We illustrate the capabilities and potential of our framework by applying it to two real datasets. Furthermore, the events detected by the framework is supplemented by extraction and investigation of the topics discovered for each community.