A Dynamic Modularity Based Community Detection Algorithm for Large-scale Networks

Aktunc, Riza; Toroslu, İsmail Hakkı; Özer, Mert; Davulcu, Hasan

doi:10.1145/2808797.2808822

Cited by 30 publications

(32 citation statements)

References 25 publications

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“…This work should be pursued further, notably by considering versions of Louvain or Infomap [4,2,17] recently introduced and dedicated to dynamic networks. However it is not certain that they achieve better results when the community structure is very evolving since they change the initialization in order to force stability by beginning the detection at time t with the partition obtained at time t − 1.…”

Section: Resultsmentioning

confidence: 99%

“…Several approaches have been introduced to detect communities in dynamic networks among which, we can mention stochastic blockmodels [43], [26], [44] clique percolation method extension [35], quality function optimization notably adaptations of CNM algorithm [34], Louvain or Infomap methods [4,2,17]. In this article, we suppose that the community detection in a dynamic network aims to identify a unique partition of the vertices into non overlapping clusters.…”

Section: Approaches For Community Mining In Dynamic Networkmentioning

confidence: 99%

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Community detection in dynamic graphs with missing edges

Benyahia¹,

Largeron²,

Jeudy³

2017

2017 11th International Conference on Research Challenges in Information Science (RCIS)

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Social networks are usually analyzed and mined without taking into account the presence of missing values. In this article, we consider dynamic networks represented by sequences of graphs that change over time and we study the robustness and the accuracy of the community detection algorithms in presence of missing edges. We assume that the network evolution can provide a complementary information allowing to neutralize the missing data. To confirm our hypothesis, we designed an experimental framework to simulate the missing data and compare the communities identified by the methods, with or without missing links. We explore two types of methods. The first ones, based on tensor decomposition, are adapted for dynamic networks. The second ones correspond to conventional community detection algorithms able to handle simple graphs. In our framework, the latter ones are adapted to dynamic graphs, either by merging the data during the preprocessing step or by merging the partitions during a post-processing step. The experimentation was conducted on synthetic and real dynamic networks for which the ground truth is available. The results confirm the best performances of the methods suited for dynamic networks when they present a complex community structure.

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Section: Resultsmentioning

confidence: 99%

Section: Approaches For Community Mining In Dynamic Networkmentioning

confidence: 99%

Community detection in dynamic graphs with missing edges

Benyahia¹,

Largeron²,

Jeudy³

2017

2017 11th International Conference on Research Challenges in Information Science (RCIS)

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“…Maillard et al [13] propose a modularity-based incremental approach extending upon the classical Clauset-Newman-Moore static method [17]. Aktunc et al [3] propose a method DSLM as an extension of its static predecessor [19]. Xie et al [8] present an incremental method based on label propagation which is a fast heuristic.…”

Section: Related Workmentioning

confidence: 99%

“…Owing to the increasing availability of dynamic networks, the problem of dynamic community detection has become an actively researched topic of late, and multiple methods have been proposed over the last decade (e.g., [3]- [5]). In Section II we present a brief review of such related works.…”

Section: Introductionmentioning

confidence: 99%

A fast and efficient incremental approach toward dynamic community detection

Zarayeneh

Kalyanaraman

2019

Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

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Community detection is a discovery tool used by network scientists to analyze the structure of real-world networks. It seeks to identify natural divisions that may exist in the input networks that partition the vertices into coherent modules (or communities). While this problem space is rich with efficient algorithms and software, most of this literature caters to the static use-case where the underlying network does not change. However, many emerging real-world use-cases give rise to a need to incorporate dynamic graphs as inputs.In this paper, we present a fast and efficient incremental approach toward dynamic community detection. The key contribution is a generic technique called ∆-screening, which examines the most recent batch of changes made to an input graph and selects a subset of vertices to reevaluate for potential community (re)assignment. This technique can be incorporated into any of the community detection methods that use modularity as its objective function for clustering. For demonstration purposes, we incorporated the technique into two well-known community detection tools. Our experiments demonstrate that our new incremental approach is able to generate performance speedups without compromising on the output quality (despite its heuristic nature). For instance, on a real-world network with 63M temporal edges (over 12 time steps), our approach was able to complete in 1056 seconds, yielding a 3× speedup over a baseline implementation. In addition to demonstrating the performance benefits, we also show how to use our approach to delineate appropriate intervals of temporal resolutions at which to analyze an input network.

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“…Real-world OSNs, however, are definitely not static. The networks formed in services such as Twitter undergo major and rapid changes over time, which places them in the field of dynamic networks (Asur, Parthasarathy & Ucar, 2007;Palla, Barabasi & Vicsek, 2007;Takaffoli et al, 2011;Tantipathananandh, Berger-Wolf & Kempe, 2007;Roy Chowdhury & Sukumar, 2014;Gauvin, Panisson & Cattuto, 2014;Greene, Doyle & Cunningham, 2010;Aktunc et al, 2015;Albano, Guillaume & Le Grand, 2014). These changes are manifested as users join in or leave one or more communities, by friends mentioning each other to attract attention or by new users referencing a total stranger.…”

Section: Introductionmentioning

confidence: 99%

Exploring Twitter communication dynamics with evolving community analysis

Konstantinidis

Papadopoulos

Kompatsiaris

2017

PeerJ Computer Science

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Online Social Networks (OSNs) have been widely adopted as a means of news dissemination, event reporting, opinion expression and discussion. As a result, news and events are being constantly reported and discussed online through OSNs such as Twitter. However, the variety and scale of all the information renders manual analysis extremely cumbersome, and therefore creating a storyline for an event or news story is an effort-intensive task. The main challenge pertains to the magnitude of data to be analyzed. To this end, we propose a framework for ranking the resulting communities and their metadata on the basis of structural, contextual and evolutionary characteristics such as community centrality, textual entropy, persistence and stability. We apply the proposed framework on three Twitter datasets and demonstrate that the analysis that followed enables the extraction of new insights with respect to influential user accounts, topics of discussion and emerging trends. These insights could primarily assist the work of social and political analysis scientists and the work of journalists in their own story telling, but also highlight the limitations of existing analysis methods and pose new research questions. To our knowledge, this study is the first to investigate the ranking of dynamic communities. In addition, our findings suggest future work regarding the determination of the general context of the communities based on structure and evolutionary behavior alone.

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A Dynamic Modularity Based Community Detection Algorithm for Large-scale Networks

Cited by 30 publications

References 25 publications

Community detection in dynamic graphs with missing edges

Community detection in dynamic graphs with missing edges

A fast and efficient incremental approach toward dynamic community detection

Exploring Twitter communication dynamics with evolving community analysis

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