Using Geodesic Space Density Gradients for Network Community Detection

Mahmood, Arif; Small, Michael; Al-Maadeed, Somaya; Rajpoot, Nasir M.

doi:10.1109/tkde.2016.2632716

Cited by 34 publications

(13 citation statements)

References 47 publications

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“…k-means clustering is a common clustering method. In the community detection problem, the similarity of two nodes is defined by shortest-path distance (Mahmood et al 2017), random walk (Pons and Latapy 2005;Rosvall and Bergstrom 2008), and so forth. In recent years, network embedding based clustering methods have emerged, where nodes in the network are firstly represented as low-dimensional vectors and then clustered into communities, such as DeepWalk (Perozzi, Al-Rfou, and Skiena 2014) and GraRep (Cao, Lu, and Xu 2015).…”

Section: Related Workmentioning

confidence: 99%

Forbidden Nodes Aware Community Search

Wang

Zhu

2019

AAAI

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Community search is an important problem in network analysis, which has attracted much attention in recent years. It starts with some given nodes, pays more attention to local network structures, and gets personalized resultant communities quickly. In this paper, we argue that there are many real scenarios where some nodes are not allowed to appear in the community. Then, we introduce a new concept called forbidden nodes and present a new problem of forbidden nodes aware community search to describe these scenarios.To address the above problem, three methods are proposed, i.e., k-core based FORTE (Forbidden nOdes awaRe communiTy sEarch), k-truss based FORTE and CW based FORTE, where the effects of both forbidden nodes and query nodes are thoroughly considered for each node in the resultant community. The former two methods are able to make use of popular community structures, while the latter is based on a new metric called weighted conductance. The extensive experiments conducted on real data sets demonstrate the effectiveness of the proposed methods.

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Section: Related Workmentioning

confidence: 99%

Forbidden Nodes Aware Community Search

Wang

Zhu

2019

AAAI

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“…The community in this case, a common work association, exhibits hierarchical rather than simply dense link structure. Moreover, the nodes group within the same region of geodesic space also can be called as the community (Mahmood and Small 2015;Mahmood et al 2016). Hence, based on different kinds of real-world communities, we argue an intuitive generalisation of the existing definition of community structure.…”

Section: Introductionmentioning

confidence: 99%

A novel metric for community detection

Shang¹,

Small²,

Wang³

et al. 2020

EPL

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Research into detection of dense communities has recently attracted increasing attention within network science, various metrics for detection of such communities have been proposed. The most popular metric -Modularity -is based on the so-called rule that the links within communities are denser than external links among communities, has become the default. However, this default metric suffers from ambiguity, and worse, all augmentations of modularity and based on a narrow intuition of what it means to form a "community". We argue that in specific, but quite common systems, links within a community are not necessarily more common than links between communities. Instead we propose that the defining characteristic of a community is that links are more predictable within a community rather than between communities. In this paper, based on the effect of communities on link prediction, we propose a novel metric for the community detection based directly on this feature. We find that our metric is more robustness than traditional modularity. Consequently, we can achieve an evaluation of algorithm stability for the same detection algorithm in different networks. Our metric also can directly uncover the false community detection, and infer more statistical characteristics for detection algorithms.

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“…While the problem is not uniquely defined [18], it can be generically described as the problem of determining whether there exists a meaningful partition of the network nodes into groups of nodes. The problem (also known as clustering in computer science [19,23]) has a long history, and it has been traditionally addressed using structural, static network analysis. S * t t  .…”

Section: Introductionmentioning

confidence: 99%

Optimal timescale for community detection in growing networks

2019

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Time-stamped data are increasingly available for many social, economic, and information systems that can be represented as networks growing with time. The World Wide Web, social contact networks, and citation networks of scientific papers and online news articles, for example, are of this kind. Static methods can be inadequate for the analysis of growing networks as they miss essential information on the system's dynamics. At the same time, time-aware methods require the choice of an observation timescale, yet we lack principled ways to determine it. We focus on the popular community detection problem which aims to partition a network's nodes into meaningful groups. We use a multi-layer quality function to show, on both synthetic and real datasets, that the observation timescale that leads to optimal communities is tightly related to the system's intrinsic aging timescale that can be inferred from the time-stamped network data. The use of temporal information leads to drastically different conclusions on the community structure of real information networks, which challenges the current understanding of the large-scale organization of growing networks. Our findings indicate that before attempting to assess structural patterns of evolving networks, it is vital to uncover the timescales of the dynamical processes that generated them.A popular approach to community detection is to maximize a function, called modularity, which quantifies how much the total number of intra-community edges deviates from its expected value under a null model that preserves the individual nodes' number of connections [24]. Modularity has been studied from many viewpoints, and it is widely-recognized as a standard tool in network analysis [18]. Despite past research and the wide use of modularity optimization in a broad range of contexts, we still lack a systematic understanding of its behavior and performance in growing networks where time and aging phenomena are fundamental [25][26][27]. Albeit modularity has been used in such systems in its original form [28][29][30], the results can be expected to be suboptimal as modularity neglects the vital time information. A multi-layer form of modularity has been developed that can take into account network snapshots at various times [31,32]. However, when we wish to apply a multi-layer approach to identify relevant communities in growing networks, we face an impasse: existing works assume layered input data [31][32][33][34] and thus they do not consider the question of how to divide an arbitrary time-stamped network into layers. Addressing this question requires to choose an appropriate observation timescale, i.e. the temporal duration for each layer [5,35,36]. This choice is essential because different timescales might reveal substantially different community structures, which in turn might lead to different conclusions on the large-scale organization of the system.In this work, we derive analytically a criterion to estimate when a time-aggregated, static view of a growing network ceases to be ...

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Using Geodesic Space Density Gradients for Network Community Detection

Cited by 34 publications

References 47 publications

Forbidden Nodes Aware Community Search

Forbidden Nodes Aware Community Search

A novel metric for community detection

Optimal timescale for community detection in growing networks

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