Community detection in social networks using ant colony algorithm and fuzzy clustering

2015 ◽  
Author(s):  
Ehsan Noveiri ◽  
Marjan Naderan ◽  
Seyed Enayatollah Alavi
Keyword(s):  
Social Networks ◽  
Community Detection ◽  
Fuzzy Clustering ◽  
Ant Colony Algorithm ◽  
Ant Colony

ACFC: ant colony with fuzzy clustering algorithm for community detection in social networks

2019 ◽  
Vol 31 (1) ◽  
pp. 36 ◽  
Author(s):  
Ehsan Noveiri ◽  
Marjan Naderan ◽  
Seyed Enayatollah Alavi
Keyword(s):  
Social Networks ◽  
Community Detection ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Ant Colony ◽  
Fuzzy Clustering Algorithm

ACFC: Ant Colony with Fuzzy Clustering Algorithm for Community Detection in Social Networks

2017 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
Marjan Naderan ◽  
Marjan Naderan ◽  
Seyed Enayatollah Alavi
Keyword(s):  
Social Networks ◽  
Community Detection ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Ant Colony ◽  
Fuzzy Clustering Algorithm

An improved ant colony algorithm for fuzzy clustering in image segmentation

2007 ◽  
Vol 70 (4-6) ◽  
pp. 665-671 ◽  
Author(s):  
Yanfang Han ◽  
Pengfei Shi
Keyword(s):  
Image Segmentation ◽  
Fuzzy Clustering ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Improved Ant Colony Algorithm

Integrating Ant Colony Algorithm and Node Centrality to Improve Prediction of Information Diffusion in Social Networks

2018 ◽  
pp. 381-391 ◽  
Author(s):  
Kasra Majbouri Yazdi ◽  
Adel Majbouri Yazdi ◽  
Saeid Khodayi ◽  
Jingyu Hou ◽  
Wanlei Zhou ◽  
...  
Keyword(s):  
Social Networks ◽  
Information Diffusion ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Node Centrality

scholarly journals Oracle Recognition of Oracle Network Based on Ant Colony Algorithm

2021 ◽  
Vol 9 ◽  
Author(s):  
Xianjin Shi ◽  
Xiajiong Shen
Keyword(s):  
Social Networks ◽  
Ant Colony Algorithm ◽  
Recognition Rate ◽  
Recommendation Systems ◽  
Recognition Algorithm ◽  
Ant Colony ◽  
Experimental Simulation ◽  
Maximum Matching ◽  
Matching Problem ◽  
Matching Degree

Recent studies have shown that compared with traditional social networks, networks in which users socialize through interest recommendation have obvious homogeneity characteristics. Recommending topics of interest to users has become one of the main objectives of recommendation systems in such social networks, and the widespread data sparsity in such social networks has become the main problem faced by such recommendation systems. Particularly, in the oracle interest network, this problem is more difficult to solve because there are very few people who read and understand the Oracle. To address this problem, we propose an ant colony algorithm based recognition algorithm that can greatly expand the data in the oracle interest network and thus improve the efficiency of oracle interest network recommendation in this paper. Using the one-to-one correspondence between characters and translation in Oracle rubbings, the Oracle recognition problem is transformed into character matching problem, which can skip manual feature engineering experts, so as to realize efficient Oracle recognition. First, the coordinates of each character in the oracle bones are extracted. Then, the matching degree value of each oracle character corresponding to the translation of the oracle rubbings is assigned according to the coordinates. Finally, the maximum matching degree value of each character is searched using the improved ant colony algorithm, and the search result is the Chinese character corresponding to the oracle rubbings. In this paper, through experimental simulation, it is proved that this method is very effective when applied to the field of oracle recognition, and the recognition rate can approach 100% in some special oracle rubbings.

A multi-objective ant colony optimization with decomposition for community detection in complex networks

2019 ◽  
Vol 41 (9) ◽  
pp. 2521-2534 ◽  
Author(s):  
Ruochen Liu ◽  
Jiangdi Liu ◽  
Manman He
Keyword(s):  
Complex Networks ◽  
Ant Colony Optimization ◽  
Community Detection ◽  
Large Scale ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Network Clustering ◽  
Multi Objective ◽  
Search Mechanism ◽  
Large Scale Networks

Community detection in complex networks plays an important role in mining and analyzing the structure and function of networks. However, traditional algorithms for community detection-based graph partition and hierarchical clustering usually have to face expensive computational costs or require some specific conditions when dealing with complex networks. Recently, community detection based on intelligent optimization attracts more and more attention because of its good effectiveness. In this paper, a new multi-objective ant colony optimization with decomposition (MACOD) for community detection in complex networks is proposed. Firstly, a new framework of multi-objective ant colony algorithm specialized initially for the complex network clustering is developed, in which two-objective optimization problem can be decomposed into a series of subproblems and each ant is responsible for one single objective subproblem and it targets a particular point in the Pareto front. Secondly, a problem-specific individual encoding strategy based on graph is proposed. Moreover, a new efficient local search mechanism is designed in order to improve the stability of the algorithm. The proposed MACOD has been compared with four other state of the art algorithms on two benchmark networks and seven real-world networks including three large-scale networks. Experimental results show that MACOD performs competitively for the community detection problems.

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