scholarly journals Parallel Conical Area Community Detection Using Evolutionary Multi-Objective Optimization

Processes ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 111 ◽  
Author(s):  
Weiqin Ying ◽  
Hassan Jalil ◽  
Bingshen Wu ◽  
Yu Wu ◽  
Zhenyu Ying ◽  
...  
Keyword(s):  
Community Detection ◽  
Migration Policy ◽  
Detection Algorithm ◽  
Island Model ◽  
Multi Objective Optimization ◽  
Community Structures ◽  
Multi Objective ◽  
Local Search Strategy ◽  
Community Detection Algorithm ◽  
Global Selection

Detecting community structures helps to reveal the functional units of complex networks. In this paper, the community detection problem is regarded as a modularity-based multi-objective optimization problem (MOP), and a parallel conical area community detection algorithm (PCACD) is designed to solve this MOP effectively and efficiently. In consideration of the global properties of the selection and update mechanisms, PCACD employs a global island model and targeted elitist migration policy in a conical area evolutionary algorithm (CAEA) to discover community structures at different resolutions in parallel. Although each island is assigned only a portion of all sub-problems in the island model, it preserves a complete population to accomplish the global selection and update. Meanwhile the migration policy directly migrates each elitist individual to an appropriate island in charge of the sub-problem associated with this individual to share essential evolutionary achievements. In addition, a modularity-based greedy local search strategy is also applied to accelerate the convergence rate. Comparative experimental results on six real-world networks reveal that PCACD is capable of discovering potential high-quality community structures at diverse resolutions with satisfactory running efficiencies.

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.

Community detection based on preferred mode in bipartite networks

2018 ◽  
Vol 32 (27) ◽  
pp. 1850330
Author(s):  
Guolin Wu ◽  
Changgui Gu ◽  
Lu Qiu ◽  
Huijie Yang
Keyword(s):  
Community Detection ◽  
Real World ◽  
Detection Algorithm ◽  
Bipartite Networks ◽  
Community Structures ◽  
Large Size ◽  
Community Information ◽  
Community Detection Algorithm

Identifying community structures in bipartite networks is a popular topic. People usually focus on one of two modes in bipartite networks when uncovering their community structures. According to this understanding, we design a community detection algorithm based on preferred mode in bipartite networks. This algorithm can select corresponding preferred mode according to specific application scenario and effectively extract community information in bipartite networks. The trials in artificial and real-world networks show that the algorithm based on preferred mode has better performances in both small size of bipartite networks and large size of bipartite networks.

Parallel Knowledge Community Detection Algorithm Research Based on MapReduce

2011 ◽  
Vol 204-210 ◽  
pp. 1646-1650 ◽  
Author(s):  
Min Xu ◽  
Pan Pan Yang ◽  
Jie Ma
Keyword(s):  
Web Services ◽  
Knowledge Discovery ◽  
Parallel Algorithm ◽  
Complex Network ◽  
Community Detection ◽  
Detection Algorithm ◽  
Community Structures ◽  
Knowledge Community ◽  
Community Detection Algorithm

Knowledge community detection algorithm is a main technical means to knowledge discovery in complex network. It is used to discover the potential community structures to provide a basis for web services, for example, personalized recommendations. In this paper, a parallel algorithm for knowledge community detection based on MapReduce is proposed. This paper, through various experiments, proves that the efficiency of the parallel algorithm is increased significantly.

scholarly journals A Fast Overlapping Community Detection Algorithm with Self-Correcting Ability

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Laizhong Cui ◽  
Lei Qin ◽  
Nan Lu
Keyword(s):  
Community Detection ◽  
Detection Algorithm ◽  
Community Structures ◽  
Overlapping Community Detection ◽  
Test Conditions ◽  
Overlapping Community ◽  
Community Detection Algorithm ◽  
Evaluation Measurement ◽  
Repeat Visits ◽  
Membership Vector

Due to the defects of all kinds of modularity, this paper defines a weighted modularity based on the density and cohesion as the new evaluation measurement. Since the proportion of the overlapping nodes in network is very low, the number of the nodes’ repeat visits can be reduced by signing the vertices with the overlapping attributes. In this paper, we propose three test conditions for overlapping nodes and present a fast overlapping community detection algorithm with self-correcting ability, which is decomposed into two processes. Under the control of overlapping properties, the complexity of the algorithm tends to be approximate linear. And we also give a new understanding on membership vector. Moreover, we improve the bridgeness function which evaluates the extent of overlapping nodes. Finally, we conduct the experiments on three networks with well known community structures and the results verify the feasibility and effectiveness of our algorithm.

scholarly journals A flexible methodology to sectorize water supply networks based on social network theory concepts and multi-objective optimization

2015 ◽  
Vol 18 (1) ◽  
pp. 62-76 ◽  
Author(s):  
Enrique Campbell ◽  
Joaquín Izquierdo ◽  
Idel Montalvo ◽  
Amilkar Ilaya-Ayza ◽  
Rafael Pérez-García ◽  
...  
Keyword(s):  
Social Network ◽  
Water Supply ◽  
Community Detection ◽  
Network Theory ◽  
Detection Algorithm ◽  
Social Network Theory ◽  
Multi Objective Optimization ◽  
Supply Networks ◽  
Multi Objective ◽  
Water Supply Networks

A novel methodology to sectorize water supply networks (WSNs) depending on a main transmission line is presented in this paper. The methodology is based on concepts derived from the social network theory and graph theory (namely, community detection and shortest path respectively); and also on a multi-objective optimization process by means of agent swarm optimization (ASO). A series of energy, operative, and economic criteria are optimized in this process. The core idea is to form sectors over the distribution network based on communities found using a community detection algorithm (Walktrap). The methodology is flexible and enables the technical staff in water utilities to make decisions at different stages. It has been tested by generating four feasible solutions over a portion of a real WSN.

Sign in / Sign up

Export Citation Format

Share Document