scholarly journals Graph Community Discovery Algorithms in Neo4j with a Regularization-based Evaluation Metric

2017 ◽  
Author(s):  
Andreas Kanavos ◽  
Georgios Drakopoulos ◽  
Athanasios Tsakalidis
Keyword(s):  
Community Discovery ◽  
Evaluation Metric ◽  
Discovery Algorithms

scholarly journals Overlapping Community Discovery Method Based on Two Expansions of Seeds

Symmetry ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 18
Author(s):  
Yan Li ◽  
Jing He ◽  
Youxi Wu ◽  
Rongjie Lv
Keyword(s):  
Symmetric Matrix ◽  
Fitness Function ◽  
Local Maximum ◽  
Community Discovery ◽  
Network Nodes ◽  
Overlapping Community ◽  
The Common ◽  
New Research ◽  
Discovery Method ◽  
Discovery Algorithms

The real world can be characterized as a complex network sto in symmetric matrix. Community discovery (or community detection) can effectively reveal the common features of network groups. The communities are overlapping since, in fact, one thing often belongs to multiple categories. Hence, overlapping community discovery has become a new research hotspot. Since the results of the existing community discovery algorithms are not robust enough, this paper proposes an effective algorithm, named Two Expansions of Seeds (TES). TES adopts the topological feature of network nodes to find the local maximum nodes as the seeds which are based on the gravitational degree, which makes the community discovery robust. Then, the seeds are expanded by the greedy strategy based on the fitness function, and the community cleaning strategy is employed to avoid the nodes with negative fitness so as to improve the accuracy of community discovery. After that, the gravitational degree is used to expand the communities for the second time. Thus, all nodes in the network belong to at least one community. Finally, we calculate the distance between the communities and merge similar communities to obtain a less- undant community structure. Experimental results demonstrate that our algorithm outperforms other state-of-the-art algorithms.

scholarly journals X-Mark: a benchmark for node-attributed community discovery algorithms

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Salvatore Citraro ◽  
Giulio Rossetti
Keyword(s):  
State Of The Art ◽  
Graph Clustering ◽  
Clustering Methods ◽  
Community Discovery ◽  
Attributed Graphs ◽  
Node Labels ◽  
Discovery Algorithms ◽  
Shed Light

AbstractGrouping well-connected nodes that also result in label-homogeneous clusters is a task often known as attribute-aware community discovery. While approaching node-enriched graph clustering methods, rigorous tools need to be developed for evaluating the quality of the resulting partitions. In this work, we present X-Mark, a model that generates synthetic node-attributed graphs with planted communities. Its novelty consists in forming communities and node labels contextually while handling categorical or continuous attributive information. Moreover, we propose a comparison between attribute-aware algorithms, testing them against our benchmark. Accordingly to different classification schema from recent state-of-the-art surveys, our results suggest that X-Mark can shed light on the differences between several families of algorithms.

scholarly journals ANGEL: efficient, and effective, node-centric community discovery in static and dynamic networks

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Giulio Rossetti
Keyword(s):  
Social Network ◽  
Dynamic Networks ◽  
Community Discovery ◽  
Social Network Data ◽  
Challenging Tasks ◽  
Interesting Family ◽  
Effectiveness And Efficiency ◽  
Art Community ◽  
Real World Datasets ◽  
Discovery Algorithms

Abstract Community discovery is one of the most challenging tasks in social network analysis. During the last decades, several algorithms have been proposed with the aim of identifying communities in complex networks, each one searching for mesoscale topologies having different and peculiar characteristics. Among such vast literature, an interesting family of Community Discovery algorithms, designed for the analysis of social network data, is represented by overlapping, node-centric approaches. In this work, following such line of research, we propose Angel, an algorithm that aims to lower the computational complexity of previous solutions while ensuring the identification of high-quality overlapping partitions. We compare Angel, both on synthetic and real-world datasets, against state of the art community discovery algorithms designed for the same community definition. Our experiments underline the effectiveness and efficiency of the proposed methodology, confirmed by its ability to constantly outperform the identified competitors.

scholarly journals Community Detection Method Based on Two-layer Dissimilarity of Central Node

2019 ◽  
Author(s):  
Yuexia Zhang ◽  
Ziyang Chen
Keyword(s):  
Public Opinion ◽  
Community Detection ◽  
Clustering Algorithm ◽  
Detection Method ◽  
Dissimilarity Index ◽  
Community Discovery ◽  
Central Node ◽  
And Control ◽  
Discovery Algorithms

Studying community discovery algorithms for complex networks is necessary to determine the origin of opinions, analyze the mechanisms of public opinion transmission, and control the evolution of public opinion. The problem of the existing clustering algorithm of the central node having a low quality of community detection must also be solved. This study proposes a community detection method based on the two-layer dissimilarity of the central node (TDCN-CD). First, the algorithm selects the central node through the degree and distance of the node. Selecting nodes in the same community as the central node at the same time is avoided. Simultaneously, the algorithm proposes the dissimilarity index of nodes based on two layers, which can deeply explore the heterogeneity of nodes and achieve the effect of accurate community division. The results of using Karate and Dolphins datasets for simulation show that compared to the Girvan–Newman and Fast–Newman classical community partitioning algorithms, the TDCN-CD algorithm can effectively detect the community structure and more accurately divide the community.  

scholarly journals A propagation-based seed-centric local community detection for multilayer environment: The case study of colon adenocarcinoma

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0255718
Author(s):  
Ehsan Pournoor ◽  
Zaynab Mousavian ◽  
Abbas Nowzari-Dalini ◽  
Ali Masoudi-Nejad
Keyword(s):  
Local Community ◽  
Single Layer ◽  
Colon Adenocarcinoma ◽  
Functional Enrichment ◽  
Community Discovery ◽  
Local Methods ◽  
Local Community Detection ◽  
Novel Method ◽  
Discovery Algorithms

Regardless of all efforts on community discovery algorithms, it is still an open and challenging subject in network science. Recognizing communities in a multilayer network, where there are several layers (types) of connections, is even more complicated. Here, we concentrated on a specific type of communities called seed-centric local communities in the multilayer environment and developed a novel method based on the information cascade concept, called PLCDM. Our simulations on three datasets (real and artificial) signify that the suggested method outstrips two known earlier seed-centric local methods. Additionally, we compared it with other global multilayer and single-layer methods. Eventually, we applied our method on a biological two-layer network of Colon Adenocarcinoma (COAD), reconstructed from transcriptomic and post-transcriptomic datasets, and assessed the output modules. The functional enrichment consequences infer that the modules of interest hold biomolecules involved in the pathways associated with the carcinogenesis.

Research on the Community Detection Methods in Complex Networks

2014 ◽  
Vol 513-517 ◽  
pp. 2433-2438
Author(s):  
Lu Wang ◽  
Yong Quan Liang ◽  
Jie Yang ◽  
Chao Song ◽  
Shu Han Cheng
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Community Detection ◽  
Basic Principle ◽  
Interesting Property ◽  
Test Methods ◽  
Detection Methods ◽  
Community Discovery ◽  
Discovery Algorithms ◽  
Important Progress

In recent 15 years, the study of complex networks has been gradually becoming an important issue. Community structure is an interesting property of complex networks. Researchers have made much exciting and important progress in community detection methods. The paper introduced the definition and significance of community structure; elaborates on the overview of community discovery algorithms and a proposed taxonomy according to the basic principle that they used. Modularity function was recommended briefly. Finally, described several popular test methods and benchmarks.

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