A new metric to compare local community detection algorithms in social networks using geodesic distance

2021 ◽  
Author(s):  
Sahar Bakhtar ◽  
Hovhannes A. Harutyunyan
Keyword(s):  
Social Networks ◽  
Community Detection ◽  
Local Community ◽  
Geodesic Distance ◽  
Detection Algorithms ◽  
Local Community Detection

scholarly journals From Community Detection to Mentor Selection in Rating-Free Collaborative Filtering

2011 ◽  
Vol 2011 ◽  
pp. 1-19
Author(s):  
Armelle Brun ◽  
Sylvain Castagnos ◽  
Anne Boyer
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Community Detection ◽  
Local Community ◽  
State Of The Art ◽  
New Approach ◽  
Detection Algorithms ◽  
Local Community Detection ◽  
The Web ◽  
Filtering Approach

The number of items that users can now access when navigating on the Web is so huge that these might feel lost. Recommender systems are a way to cope with this profusion of data by suggesting items that fit the users needs. One of the most popular techniques for recommender systems is the collaborative filtering approach that relies on the preferences of items expressed by users, usually under the form of ratings. In the absence of ratings, classical collaborative filtering techniques cannot be applied. Fortunately, the behavior of users, such as their consultations, can be collected. In this paper, we present a new approach to perform collaborative filtering when no rating is available but when user consultations are known. We propose to take inspiration from local community detection algorithms to form communities of users and deduce the set of mentors of a given user. We adapt one state-of-the-art algorithm so as to fit the characteristics of collaborative filtering. Experiments conducted show that the precision achieved is higher then the baseline that does not perform any mentor selection. In addition, our model almost offsets the absence of ratings by exploiting a reduced set of mentors.

scholarly journals Local Community Detection Algorithm Based on Minimal Cluster

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Yong Zhou ◽  
Guibin Sun ◽  
Yan Xing ◽  
Ranran Zhou ◽  
Zhixiao Wang
Keyword(s):  
Community Detection ◽  
Local Community ◽  
Detection Algorithm ◽  
Single Node ◽  
Initial Node ◽  
Detection Algorithms ◽  
Local Community Detection ◽  
Two Phases ◽  
Community Detection Algorithm

In order to discover the structure of local community more effectively, this paper puts forward a new local community detection algorithm based on minimal cluster. Most of the local community detection algorithms begin from one node. The agglomeration ability of a single node must be less than multiple nodes, so the beginning of the community extension of the algorithm in this paper is no longer from the initial node only but from a node cluster containing this initial node and nodes in the cluster are relatively densely connected with each other. The algorithm mainly includes two phases. First it detects the minimal cluster and then finds the local community extended from the minimal cluster. Experimental results show that the quality of the local community detected by our algorithm is much better than other algorithms no matter in real networks or in simulated networks.

Optimal local community detection in social networks based on density drop of subgraphs

2014 ◽  
Vol 36 ◽  
pp. 46-53 ◽  
Author(s):  
Xingqin Qi ◽  
Wenliang Tang ◽  
Yezhou Wu ◽  
Guodong Guo ◽  
Eddie Fuller ◽  
...  
Keyword(s):  
Social Networks ◽  
Community Detection ◽  
Local Community ◽  
Local Community Detection

scholarly journals Multiple Local Community Detection via High-Quality Seed Identification over Both Static and Dynamic Networks

2021 ◽  
Author(s):  
Jiaxu Liu ◽  
Yingxia Shao ◽  
Sen Su
Keyword(s):  
Community Detection ◽  
Real World ◽  
Local Community ◽  
Dynamic Networks ◽  
Dynamic Network ◽  
High Quality ◽  
Seed Node ◽  
Detection Algorithms ◽  
Local Community Detection ◽  
High Quality Seed

AbstractLocal community detection aims to find the communities that a given seed node belongs to. Most existing works on this problem are based on a very strict assumption that the seed node only belongs to a single community, but in real-world networks, nodes are likely to belong to multiple communities. In this paper, we first introduce a novel algorithm, HqsMLCD, that can detect multiple communities for a given seed node over static networks. HqsMLCD first finds the high-quality seeds which can detect better communities than the given seed node with the help of network representation, then expands the high-quality seeds one-by-one to get multiple communities, probably overlapping. Since dynamic networks also act an important role in practice, we extend the static HqsMLCD to handle dynamic networks and introduce HqsDMLCD. HqsDMLCD mainly integrates dynamic network embedding and dynamic local community detection into the static one. Experimental results on real-world networks demonstrate that our new method HqsMLCD outperforms the state-of-the-art multiple local community detection algorithms. And our dynamic method HqsDMLCD gets comparable results with the static method on real-world networks.

scholarly journals Approaching the Optimal Solution of the Maximal α-quasi-clique Local Community Problem

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1438
Author(s):  
Patricia Conde-Cespedes
Keyword(s):  
Graph Theory ◽  
Community Detection ◽  
Local Community ◽  
Optimal Solution ◽  
Local Communities ◽  
Optimal Value ◽  
Interesting Task ◽  
Local Community Detection ◽  
Networks Analysis ◽  
Node Problem

Complex networks analysis (CNA) has attracted so much attention in the last few years. An interesting task in CNA complex network analysis is community detection. In this paper, we focus on Local Community Detection, which is the problem of detecting the community of a given node of interest in the whole network. Moreover, we study the problem of finding local communities of high density, known as α-quasi-cliques in graph theory (for high values of α in the interval ]0,1[). Unfortunately, the higher α is, the smaller the communities become. This led to the maximal α-quasi-clique community of a given node problem, which is, the problem of finding local communities that are α-quasi-cliques of maximal size. This problem is NP-hard, then, to approach the optimal solution, some heuristics exist. When α is high (>0.5) the diameter of a maximal α-quasi-clique is at most 2. Based on this property, we propose an algorithm to calculate an upper bound to approach the optimal solution. We evaluate our method in real networks and conclude that, in most cases, the bound is very accurate. Furthermore, for a real small network, the optimal value is exactly achieved in more than 80% of cases.

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