scholarly journals Community Detection in Multiplex Networks

2021 ◽  
Vol 54 (3) ◽  
pp. 1-35
Author(s):  
Matteo Magnani ◽  
Obaida Hanteer ◽  
Roberto Interdonato ◽  
Luca Rossi ◽  
Andrea Tagarelli
Keyword(s):  
Community Detection ◽  
Experimental Evaluation ◽  
Network Models ◽  
Ground Truth ◽  
Community Structures ◽  
Multiplex Networks ◽  
Detection Algorithms ◽  
Multiplex Network ◽  
The Right ◽  
Modes Of Interaction

A multiplex network models different modes of interaction among same-type entities. In this article, we provide a taxonomy of community detection algorithms in multiplex networks. We characterize the different algorithms based on various properties and we discuss the type of communities detected by each method. We then provide an extensive experimental evaluation of the reviewed methods to answer three main questions: to what extent the evaluated methods are able to detect ground-truth communities, to what extent different methods produce similar community structures, and to what extent the evaluated methods are scalable. One goal of this survey is to help scholars and practitioners to choose the right methods for the data and the task at hand, while also emphasizing when such choice is problematic.

scholarly journals Towards effective discovery of natural communities in complex networks and implications in e-commerce

2020 ◽  
Author(s):  
Swarup Chattopadhyay ◽  
Tanmay Basu ◽  
Asit K. Das ◽  
Kuntal Ghosh ◽  
Late C. A. Murthy
Keyword(s):  
Complex Networks ◽  
Community Detection ◽  
Similarity Measure ◽  
Recommender System ◽  
Data Clustering ◽  
Ground Truth ◽  
Normalized Mutual Information ◽  
Detection Algorithms ◽  
Node Similarity ◽  
Art Community

AbstractAutomated community detection is an important problem in the study of complex networks. The idea of community detection is closely related to the concept of data clustering in pattern recognition. Data clustering refers to the task of grouping similar objects and segregating dissimilar objects. The community detection problem can be thought of as finding groups of densely interconnected nodes with few connections to nodes outside the group. A node similarity measure is proposed here that finds the similarity between two nodes by considering both neighbors and non-neighbors of these two nodes. Subsequently, a method is introduced for identifying communities in complex networks using this node similarity measure and the notion of data clustering. The significant characteristic of the proposed method is that it does not need any prior knowledge about the actual communities of a network. Extensive experiments on several real world and artificial networks with known ground-truth communities are reported. The proposed method is compared with various state of the art community detection algorithms by using several criteria, viz. normalized mutual information, f-measure etc. Moreover, it has been successfully applied in improving the effectiveness of a recommender system which is rapidly becoming a crucial tool in e-commerce applications. The empirical results suggest that the proposed technique has the potential to improve the performance of a recommender system and hence it may be useful for other e-commerce applications.

scholarly journals Community Detection in a Weighted Directed Hypergraph Representation of Cell-to-cell Communication Networks

2020 ◽  
Author(s):  
Rui Hou ◽  
Michael Small ◽  
Alistair R. R. Forrest
Keyword(s):  
Communication Networks ◽  
Community Detection ◽  
Cell Communication ◽  
Cell Types ◽  
Community Structures ◽  
Simple Graphs ◽  
Detection Algorithms ◽  
Complex Processes ◽  
Community Identification ◽  
Directed Hypergraph

AbstractCell-to-cell communication is mainly triggered by ligand-receptor activities. Through ligandreceptor pairs, cells coordinate complex processes such as development, homeostasis, and immune response. In this work, we model the ligand-receptor-mediated cell-to-cell communication network as a weighted directed hypergraph. In this mathematical model, collaborating cell types are considered as a node community while the ligand-receptor pairs connecting them are considered a hyperedge community. We first define the community structures in a weighted directed hypergraph and develop an exact community detection method to identify these communities. We then modify approximate community detection algorithms designed for simple graphs to identify the nodes and hyperedges within each community. Application to synthetic hypergraphs with known community structure confirmed that one of the proposed approximate community identification strategies, named HyperCommunity algorithm, can effectively and precisely detect embedded communities. We then applied this strategy to two organism-wide datasets and identified putative community structures. Notably the method identifies non-overlapping edge-communities mediated by different sets of ligand-receptor pairs, however node-communities can overlap.

Gravitational community detection by predicting diameter

2021 ◽  
pp. 2150145
Author(s):  
Himansu Sekhar Pattanayak ◽  
Harsh K. Verma ◽  
Amrit Lal Sangal
Keyword(s):  
Community Detection ◽  
Ground Truth ◽  
Detection Algorithm ◽  
Local Information ◽  
Ground Truth Data ◽  
Overlapping Communities ◽  
Detection Algorithms ◽  
Np Hard Problem ◽  
Community Detection Algorithm ◽  
The Individual

Community detection is a pivotal part of network analysis and is classified as an NP-hard problem. In this paper, a novel community detection algorithm is proposed, which probabilistically predicts communities’ diameter using the local information of random seed nodes. The gravitation method is then applied to discover communities surrounding the seed nodes. The individual communities are combined to get the community structure of the whole network. The proposed algorithm, named as Local Gravitational community detection algorithm (LGCDA), can also work with overlapping communities. LGCDA algorithm is evaluated based on quality metrics and ground-truth data by comparing it with some of the widely used community detection algorithms using synthetic and real-world networks.

scholarly journals Incremental community discovery via latent network representation and probabilistic inference

2019 ◽  
Vol 62 (6) ◽  
pp. 2281-2300
Author(s):  
Zhe Cui ◽  
Noseong Park ◽  
Tanmoy Chakraborty
Keyword(s):  
Community Structure ◽  
Community Detection ◽  
Information Gain ◽  
Ground Truth ◽  
Neighborhood Structure ◽  
Detection Algorithms ◽  
Community Information ◽  
Privacy Constraints ◽  
Two Stages ◽  
Entire Network

AbstractMost of the community detection algorithms assume that the complete network structure $$\mathcal {G}=(\mathcal {V},\mathcal {E})$$G=(V,E) is available in advance for analysis. However, in reality this may not be true due to several reasons, such as privacy constraints and restricted access, which result in a partial snapshot of the entire network. In addition, we may be interested in identifying the community information of only a selected subset of nodes (denoted by $$\mathcal {V}_{{\mathrm{T}}} \subseteq \mathcal {V}$$VT⊆V), rather than obtaining the community structure of all the nodes in $$\mathcal {G}$$G. To this end, we propose an incremental community detection method that repeats two stages—(i) network scan and (ii) community update. In the first stage, our method selects an appropriate node in such a way that the discovery of its local neighborhood structure leads to an accurate community detection in the second stage. We propose a novel criterion, called Information Gain, based on existing network embedding algorithms (Deepwalk and node2vec) to scan a node. The proposed community update stage consists of expectation–maximization and Markov Random Field-based denoising strategy. Experiments with 5 diverse networks with known ground-truth community structure show that our algorithm achieves 10.2% higher accuracy on average over state-of-the-art algorithms for both network scan and community update steps.

scholarly journals Effectively Detecting Communities by Adjusting Initial Structure via Cores

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-20
Author(s):  
Mei Chen ◽  
Zhichong Yang ◽  
Xiaofang Wen ◽  
Mingwei Leng ◽  
Mei Zhang ◽  
...  
Keyword(s):  
Community Structure ◽  
Community Detection ◽  
Linear Time ◽  
Local Density ◽  
Ground Truth ◽  
Detection Algorithm ◽  
Initial Structure ◽  
Community Structures ◽  
Art Community ◽  
Community Detection Algorithm

Community detection is helpful to understand useful information in real-world networks by uncovering their natural structures. In this paper, we propose a simple but effective community detection algorithm, called ACC, which needs no heuristic search but has near-linear time complexity. ACC defines a novel similarity which is different from most common similarity definitions by considering not only common neighbors of two adjacent nodes but also their mutual exclusive degree. According to this similarity, ACC groups nodes together to obtain the initial community structure in the first step. In the second step, ACC adjusts the initial community structure according to cores discovered through a new local density which is defined as the influence of a node on its neighbors. The third step expands communities to yield the final community structure. To comprehensively demonstrate the performance of ACC, we compare it with seven representative state-of-the-art community detection algorithms, on small size networks with ground-truth community structures and relatively big-size networks without ground-truth community structures. Experimental results show that ACC outperforms the seven compared algorithms in most cases.

scholarly journals Constructing Real-Life Benchmarks for Community Detection by Rewiring Edges

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-16 ◽  
Author(s):  
Jing Xiao ◽  
Hong-Fei Ren ◽  
Xiao-Ke Xu
Keyword(s):  
Performance Evaluation ◽  
Community Detection ◽  
Multiple Scales ◽  
Special Functions ◽  
Structural Characteristics ◽  
Real Life ◽  
Original Network ◽  
Community Structures ◽  
Functional Characteristics ◽  
Detection Algorithms

In order to make the performance evaluation of community detection algorithms more accurate and deepen our analysis of community structures and functional characteristics of real-life networks, a new benchmark constructing method is designed from the perspective of directly rewiring edges in a real-life network instead of building a model. Based on the method, two kinds of novel benchmarks with special functions are proposed. The first kind can accurately approximate the microscale and mesoscale structural characteristics of the original network, providing ideal proxies for real-life networks and helping to realize performance analysis of community detection algorithms when a real network varies characteristics at multiple scales. The second kind is able to independently vary the community intensity in each generated benchmark and make the robustness evaluation of community detection algorithms more accurate. Experimental results prove the effectiveness and superiority of our proposed method. It enables more real-life networks to be used to construct benchmarks and helps to deepen our analysis of community structures and functional characteristics of real-life networks.

LP-LPA: A link influence-based label propagation algorithm for discovering community structures in networks

2018 ◽  
Vol 32 (06) ◽  
pp. 1850062 ◽  
Author(s):  
Kamal Berahmand ◽  
Asgarali Bouyer
Keyword(s):  
Community Detection ◽  
High Stability ◽  
Label Propagation ◽  
Community Structures ◽  
Detection Algorithms ◽  
Propagation Algorithm ◽  
Structural And Functional Properties ◽  
Random Behavior ◽  
Wide Range ◽  
Link Strength

Community detection is an essential approach for analyzing the structural and functional properties of complex networks. Although many community detection algorithms have been recently presented, most of them are weak and limited in different ways. Label Propagation Algorithm (LPA) is a well-known and efficient community detection technique which is characterized by the merits of nearly-linear running time and easy implementation. However, LPA has some significant problems such as instability, randomness, and monster community detection. In this paper, an algorithm, namely node’s label influence policy for label propagation algorithm (LP-LPA) was proposed for detecting efficient community structures. LP-LPA measures link strength value for edges and nodes’ label influence value for nodes in a new label propagation strategy with preference on link strength and for initial nodes selection, avoid of random behavior in tiebreak states, and efficient updating order and rule update. These procedures can sort out the randomness issue in an original LPA and stabilize the discovered communities in all runs of the same network. Experiments on synthetic networks and a wide range of real-world social networks indicated that the proposed method achieves significant accuracy and high stability. Indeed, it can obviously solve monster community problem with regard to detecting communities in networks.

Analysis of Multiplex Social Networks Using Nature-Inspired Algorithms

2019 ◽  
pp. 290-318 ◽  
Author(s):  
Ruchi Mittal ◽  
M. P. S. Bhatia
Keyword(s):  
Social Networks ◽  
Community Detection ◽  
Real Life ◽  
Alternative Methods ◽  
Centrality Measures ◽  
Eigenvector Centrality ◽  
Multiplex Networks ◽  
Multiplex Network ◽  
Nature Inspired Algorithms ◽  
Insight Into

Many real-life social networks are having multiple types of interaction among entities; thus, this organization in networks builds a new scenario called multiplex networks. Community detection, centrality measurements are the trendy area of research in multiplex networks. Community detection means identifying the highly connected groups of nodes in the network. Centrality measures indicate evaluating the importance of a node in a given network. Here, the authors propose their methodologies to compute the eigenvector centrality of nodes to find out the most influential nodes in the network and present their study on finding communities from multiplex networks. They combine a few popular nature-inspired algorithms with multiplex social networks to do the above tasks. The authors' experiments provide a deep insight into the various properties of the multiplex network. They compare the proposed methodologies with several alternative methods and get encouraging and comparable results.

Multilayer Network Models

2018 ◽  
Author(s):  
Ginestra Bianconi
Keyword(s):  
Network Models ◽  
Null Models ◽  
Emergent Properties ◽  
Multilayer Networks ◽  
Scale Free ◽  
Multiplex Networks ◽  
Multiplex Network ◽  
Network Properties ◽  
Randomization Algorithms ◽  
Network Ensembles

This chapter presents the existing modelling frameworks for multiplex and multilayer networks. Multiplex network models are divided into growing multiplex network models and null models of multiplex networks. Growing multiplex networks are here shown to explain the main dynamical rules responsible to the emergent properties of multiplex networks, including the scale-free degree distribution, interlayer degree correlations and multilayer communities. Null models of multiplex networks are described in the context of maximum-entropy multiplex network ensembles. Randomization algorithms to test the relevant of network properties against null models are here described. Moreover, Multi-slice temporal networks Models capturing main properties of real temporal network data are presented. Finally, null models of general multilayer networks and networks of networks are characterized.

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