A Method for Group Extraction in Complex Social Networks

Bródka, Piotr; Musiał, Katarzyna; Kazienko, Przemysław

doi:10.1007/978-3-642-16318-0_27

Cited by 50 publications

(20 citation statements)

References 28 publications

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“…Some other existing evaluation metrics also provide considerable solutions to the community detection problem in multilayer networks, such as multilayer clustering coefficient (the authors consider the overlapping of layers or the networks with multiple types of connections) (Bródka et al 2010;Battiston et al 2013), multilayer centrality (the authors consider a random walker to jump between layers through specific node pairs or edges) Lambiotte and Rosvall 2012), etc. What these methods share in common is that they assume the layers are independent or can be aggregated The position of a layer in a multilayer network can be specified by determining which aspect it belongs to and its serial number within the aspect.…”

Section: Existing Evaluation Metrics For Community Detection In Multimentioning

confidence: 99%

“…Despite numerous studies on multilayer networks in recent years, there is still a lack of evaluation metrics for measuring the community structure of a multilayer network, which in turn limits the number of available algorithms to find the optimal community structure in multilayer networks. Existing evaluation metrics in multilayer networks are mainly derived from "single-layer" cases, where the evaluation metrics are designed to detect modular structures in conventional networks that can be represented simply with nodes and edges, e.g., edge centrality, clustering coefficient, and metrics based on dynamic process (Battiston et al 2013;Bródka et al 2010;Lambiotte and Rosvall 2012;Kivelä et al 2014;De Domenico and Lancichinetti 2015). In such methods, detections are applied independently on each layers before final assignment, or on a "collapsed network" which is a single-layer network generated by aggregating the layers (Peixoto 2015).…”

Section: Introductionmentioning

confidence: 99%

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Modularity in complex multilayer networks with multiple aspects: a static perspective

et al. 2017

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Complex systems are usually illustrated by networks which captures the topology of the interactions between the entities. To better understand the roles played by the entities in the system one needs to uncover the underlying community structure of the system. In recent years, systems with interactions that have various types or can change over time between the entities have attracted an increasing research attention. However, algorithms aiming to solve the key problem -community detection -in multilayer networks are still limited. In this work, we first introduce the multilayer network model representation with multiple aspects, which is flexible to a variety of networks. Then based on this model, we naturally derive the multilayer modularity -a widely adopted objective function of community detection in networks -from a static perspective as an evaluation metric to evaluate the quality of the communities detected in multilayer networks. It enables us to better understand the essence of the modularity by pointing out the specific kind of communities that will lead to a high modularity score. We also propose a spectral method called mSpec for the optimization of the proposed modularity function based on the supra-adjacency representation of the multilayer networks. Experiments on the electroencephalograph network and the comparison results on several empirical multilayer networks demonstrate the feasibility and reliable performance of the proposed method.

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Section: Existing Evaluation Metrics For Community Detection In Multimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modularity in complex multilayer networks with multiple aspects: a static perspective

et al. 2017

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“…The clustering coefficient that was defined in [36] for node-aligned multiplex networks is > − . References [37,38] defined a family of local clustering coefficients for directed and weighted multiplex networks:…”

Section: Appendix a Weighted Clustering Coefficientsmentioning

confidence: 99%

“…Such considerations are crucial when developing multiplex generalizations of any single-layer (i.e., 'monoplex') network diagnostic. There have been several attempts to define multiplex clustering coefficients [36][37][38][39][40], but there are significant shortcomings in these definitions. For example, some of them do not reduce to the standard single-layer clustering coefficient or are not properly normalized (see appendix B).The fact that existing definitions of multiplex clustering coefficients are mostly ad hoc makes them difficult to interpret.…”

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confidence: 99%

Structure of triadic relations in multiplex networks

et al. 2015

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Recent advances in the study of networked systems have highlighted that our interconnected world is composed of networks that are coupled to each other through different "layers" that each represent one of many possible subsystems or types of interactions. Nevertheless, it is traditional to aggregate multilayer networks into a single weighted network in order to take advantage of existing tools. This is admittedly convenient, but it is also extremely problematic, as important information can be lost as a result. It is therefore important to develop multilayer generalizations of network concepts. In this paper, we analyze triadic relations and generalize the idea of transitivity to multiplex networks. By focusing on triadic relations, which yield the simplest type of transitivity, we generalize the concept and computation of clustering coefficients to multiplex networks. We show how the layered structure of such networks introduces a new degree of freedom that has a fundamental effect on transitivity. We compute multiplex clustering coefficients for several real multiplex networks and illustrate why one must take great care when generalizing standard network concepts to multiplex networks. We also derive analytical expressions for our clustering coefficients for ensemble averages of networks in a family of random multiplex networks. Our analysis illustrates that social networks have a strong tendency to promote redundancy by closing triads at every layer and that they thereby have a different type of multiplex transitivity from transportation networks, which do not exhibit such a tendency. These insights are invisible if one only studies aggregated networks.

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“…For example, on node/edge granularity, [7] and [8] focus on developing suitable centrality measures [9] like cross-layer degree centrality for multilayer network [10], [11]. Brodka et.al [12], [13] proposed multilayered local clustering coefficient (MLCC) and cross-layer clustering coefficient (CLCC) to depict cluster coefficient [14] of a node in a multilayer network. In contrast, the cluster level is often used for community detection [1], [3], [15]- [19], and the layer level used to analyze the interactions between different layer types [5], [20].…”

Section: Introductionmentioning

confidence: 99%

Principled Multilayer Network Embedding

Liu

Chen

Yeung

et al. 2017

2017 IEEE International Conference on Data Mining Workshops (ICDMW)

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Abstract-Multilayer network analysis has become a vital tool for understanding different relationships and their interactions in a complex system, where each layer in a multilayer network depicts the topological structure of a group of nodes corresponding to a particular relationship. The interactions among different layers imply how the interplay of different relations on the topology of each layer. For a single-layer network, network embedding methods have been proposed to project the nodes in a network into a continuous vector space with a relatively small number of dimensions, where the space embeds the social representations among nodes. These algorithms have been proved to have a better performance on a variety of regular graph analysis tasks, such as link prediction, or multi-label classification.In this paper, by extending a standard graph mining into multilayer network, we have proposed three methods ("network aggregation," "results aggregation" and "layer co-analysis") to project a multilayer network into a continuous vector space. On one hand, without leveraging interactions among layers, "network aggregation" and "results aggregation" apply the standard network embedding method on the merged graph or each layer to find a vector space for multilayer network. On the other hand, in order to consider the influence of interactions among layers, "layer co-analysis" expands any single-layer network embedding method to a multilayer network. By introducing the link transition probability based on information distance, this method not only uses the first and second order random walk to traverse on a layer, but also has the ability to traverse between layers by leveraging interactions. From the evaluation, we have proved that comparing with regular link prediction methods, "layer co-analysis" achieved the best performance on most of the datasets, while "network aggregation" and "results aggregation" also have better performance than regular link prediction methods.

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A Method for Group Extraction in Complex Social Networks

Abstract: This dissertation is dedicated to my parents, my brother and the rest of my family.For their endless love, encouragement and the fact that they always support my decisions no matter how crazy they seem to be.

Cited by 50 publications

References 28 publications

Modularity in complex multilayer networks with multiple aspects: a static perspective

Modularity in complex multilayer networks with multiple aspects: a static perspective

Structure of triadic relations in multiplex networks

Principled Multilayer Network Embedding

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