Ensemble-based community detection in multilayer networks

Tagarelli, Andrea; Amelio, Alessia; Gullo, Francesco

doi:10.1007/s10618-017-0528-8

Cited by 65 publications

(53 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Amelio et al proposed several methods to deal with multilayer networks 23,24 and signed networks. There are far fewer studies on community detection of attributed networks compared with that of plain graphs.…”

Section: Related Workmentioning

confidence: 99%

Detecting semantic‐based communities in node‐attributed graphs

Huang

Sun

et al. 2018

Computational Intelligence

View full text Add to dashboard Cite

In social network analysis, community detection on plain graphs has been widely studied. With the proliferation of available data, each user in the network is usually associated with additional attributes for elaborate description. However, many existing methods only concentrate on the topological structure and fail to deal with node-attributed networks. These approaches are incapable of extracting clear semantic meanings for communities detected. In this paper, we combine the topological structure and attribute information into a unified process and propose a novel algorithm to detect overlapping semantic communities. Moreover, a new metric is designed to measure the density of semantic communities. The proposed algorithm is divided into 3 phases. First, we detect local semantic subcommunities from each node's perspective using a greedy strategy on the metric. Then, a supergraph, which consists of all these subcommunities is created. Finally, we find global semantic communities on the supergraph. The experimental results on real-world data sets show the efficiency and effectiveness of our approach against other state-of-the-art methods.

show abstract

Section: Related Workmentioning

confidence: 99%

Detecting semantic‐based communities in node‐attributed graphs

Huang

Sun

et al. 2018

Computational Intelligence

View full text Add to dashboard Cite

show abstract

“…It should be emphasized that neglecting such a kind of complex organization by reducing the whole system to a single network (e.g., through some kind of projection, or by aggregation), has been shown to be much less informative than the multilayer representation [14]. For the above mentioned reasons, multilayer networks are experiencing an increasing interest from the scientific community, leading to an explosion of scientific papers in many areas of science, thus becoming one of the most used tools for interdisciplinary research [15], [16], [11], [13], [17], [18], [12], [19], [20,21], [22], [23].…”

Section: Introductionmentioning

confidence: 99%

Modularity in Multilayer Networks Using Redundancy-Based Resolution and Projection-Based Inter-Layer Coupling

Amelio

Mangioni

Tagarelli

2020

IEEE Trans. Netw. Sci. Eng.

Self Cite

View full text Add to dashboard Cite

The generalized version of modularity for multilayer networks, a.k.a. multislice modularity, is characterized by two model parameters, namely resolution factor and inter-layer coupling factor. The former corresponds to a notion of layer-specific relevance, whereas the inter-layer coupling factor represents the strength of node connections across the network layers. Despite the potential of this approach, the setting of both parameters can be arbitrarily selected, without considering specific characteristics from the topology of the multilayer network as well as from an available community structure. Also, the multislice modularity is not designed to explicitly model order relations over the layers, which is of prior importance for dynamic networks.This paper aims to overcome the main limitations of the multislice modularity by introducing a new formulation of modularity for multilayer networks. We revise the role and semantics of both the resolution and inter-layer coupling factors based on information available from the within-layer and inter-layer structures of the multilayer communities. Also, our proposed multilayer modularity is general enough to consider orderings of network layers and their constraints on layer coupling. Experiments were carried out on synthetic and real-world multilayer networks using state-of-the-art approaches for multilayer community detection. The obtained results have shown the meaningfulness of the proposed modularity, revealing the effects of different combinations of the resolution and inter-layer coupling functions. This work also represents a starting point for the development of new optimization methods for community detection in multilayer networks.

show abstract

“…An effective approach to ML-CD corresponds to aggregation methods, whose goal is to infer a community structure by combining information from community structures separately obtained on each of the layers [17,18,16]. A special class of such methods resembles theory on clustering ensemble [15,6]: given a set of clusterings as different groupings of the input data, a consensus criterion function is optimized to induce a single, meaningful solution that is representative of the input clusterings.…”

Section: Introductionmentioning

confidence: 99%

Consensus Community Detection in Multilayer Networks Using Parameter-Free Graph Pruning

Mandaglio

Amelio

Tagarelli

2018

Advances in Knowledge Discovery and Data Mining

Self Cite

View full text Add to dashboard Cite

The clustering ensemble paradigm has emerged as an effective tool for community detection in multilayer networks, which allows for producing consensus solutions that are designed to be more robust to the algorithmic selection and configuration bias. However, one limitation is related to the dependency on a co-association threshold that controls the degree of consensus in the community structure solution. The goal of this work is to overcome this limitation with a new framework of ensemblebased multilayer community detection, which features parameter-free identification of consensus communities based on generative models of graph pruning that are able to filter out noisy co-associations. We also present an enhanced version of the modularity-driven ensemble-based multilayer community detection method, in which community memberships of nodes are reconsidered to optimize the multilayer modularity of the consensus solution. Experimental evidence on real-world networks confirms the beneficial effect of using model-based filtering methods and also shows the superiority of the proposed method on state-of-the-art multilayer community detection.

show abstract

Ensemble-based community detection in multilayer networks

Cited by 65 publications

References 30 publications

Detecting semantic‐based communities in node‐attributed graphs

Detecting semantic‐based communities in node‐attributed graphs

Modularity in Multilayer Networks Using Redundancy-Based Resolution and Projection-Based Inter-Layer Coupling

Consensus Community Detection in Multilayer Networks Using Parameter-Free Graph Pruning

Contact Info

Product

Resources

About