Is it correct to project and detect? How weighting unipartite projections influences community detection

AbstractBipartite networks represent pairwise relationships between nodes belonging to two distinct classes. While established methods exist for analyzing unipartite networks, those for bipartite network analysis are somewhat obscure and relatively less developed. Community detection in such instances is frequently approached by first projecting the network onto a unipartite network, a method where edges between node classes are encoded as edges within one class. Here we test seven different projection schemes by assessing the performance of community detection on both: (i) a real-world dataset from social media and (ii) an ensemble of artificial networks with prescribed community structure. A number of performance and accuracy issues become apparent from the experimental findings, especially in the case of long-tailed degree distributions. Of the methods tested, the “hyperbolic” projection scheme alleviates most of these difficulties and is thus the most robust scheme of those tested. We conclude that any interpretation of community detection algorithm performance on projected networks must be done with care as certain network configurations require strong community preference for the bipartite structure to be reflected in the unipartite communities. Our results have implications for the analysis of detected community structure in projected unipartite networks.

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Overlapping Community Detection of Bipartite Networks Based on a Novel Community Density

Future Internet ◽

10.3390/fi13040089 ◽

2021 ◽

Vol 13 (4) ◽

pp. 89

Author(s):

Yubo Peng ◽

Bofeng Zhang ◽

Furong Chang

Keyword(s):

Community Detection ◽

Real World ◽

Detection Algorithm ◽

Point Of View ◽

Bipartite Network ◽

Bipartite Networks ◽

Overlapping Community Detection ◽

The Real ◽

Overlapping Community ◽

Community Detection Algorithm

Community detection plays an essential role in understanding network topology and mining underlying information. A bipartite network is a complex network with more important authenticity and applicability than a one-mode network in the real world. There are many communities in the network that present natural overlapping structures in the real world. However, most of the research focuses on detecting non-overlapping community structures in the bipartite network, and the resolution of the existing evaluation function for the community structure’s merits are limited. So, we propose a novel function for community detection and evaluation of the bipartite network, called community density D. And based on community density, a bipartite network community detection algorithm DSNE (Density Sub-community Node-pair Extraction) is proposed, which is effective for overlapping community detection from a micro point of view. The experiments based on artificially-generated networks and real-world networks show that the DSNE algorithm is superior to some existing excellent algorithms; in comparison, the community density (D) is better than the bipartite network’s modularity.

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Community detection by enhancing community structure in bipartite networks

Modern Physics Letters B ◽

10.1142/s0217984919500763 ◽

2019 ◽

Vol 33 (07) ◽

pp. 1950076 ◽

Cited By ~ 1

Author(s):

Wenjie Zhou ◽

Xingyuan Wang ◽

Chuan Zhang ◽

Rui Li ◽

Chunpeng Wang

Keyword(s):

Community Structure ◽

Community Detection ◽

Real World ◽

State Of The Art ◽

Detection Algorithm ◽

Original Structure ◽

Bipartite Networks ◽

Detection Algorithms ◽

Low Weight ◽

Community Detection Algorithm

Community detection is one of the primary tools to discover useful information that is hidden in complex networks. Some community detection algorithms for bipartite networks have been proposed from various viewpoints. However, the performance of these algorithms deteriorates when the community structure becomes unclear. Enhancing community structure remains a nontrivial task. In this paper, we propose a community detection algorithm, called ECD, that enhances community structure in bipartite networks. In the proposed ECD, the topology of a network is modified by reducing unnecessary edges that are connected to neighboring low-weight communities. Therefore, an ambiguous community structure is converted into a structure that is much clearer than the original structure. The experimental results on both artificial and real-world networks verify the accuracy and reliability of our algorithm. Compared with existing community detection algorithms using state-of-the-art methods, our algorithm has better performance.

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Similarity-based local community detection for bipartite networks

Filomat ◽

10.2298/fil1805559c ◽

2018 ◽

Vol 32 (5) ◽

pp. 1559-1570 ◽

Cited By ~ 1

Author(s):

Dongming Chen ◽

Wei Zhao ◽

Dongqi Wang ◽

Xinyu Huang

Keyword(s):

Community Structure ◽

Community Detection ◽

Local Community ◽

Partial Information ◽

Real Data ◽

Detection Algorithm ◽

Detection Methods ◽

Bipartite Network ◽

Bipartite Networks ◽

Local Community Detection

Local community detection aims to obtain the local communities to which target nodes belong, by employing only partial information of the network. As a commonly used network model, bipartite applies naturally when modeling relations between two different classes of objects. There are three problems to be solved in local community detection, such as initial core node selection, expansion approach and community boundary criteria. In this work, a similarity based local community detection algorithm for bipartite networks (SLCDB) is proposed, and the algorithm can be used to detect local community structure by only using either type of nodes of a bipartite network. Experiments on real data prove that SLCDB algorithms output community structure can achieve a very high modularity which outperforms most existing local community detection methods for bipartite networks.

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Node Trust: an effective method to detect non-overlapping community in social networks

Modern Physics Letters B ◽

10.1142/s0217984921500366 ◽

2020 ◽

pp. 2150036

Author(s):

Jinfang Sheng ◽

Qiong Li ◽

Bin Wang ◽

Wanghao Guan ◽

Jinying Dai ◽

...

Keyword(s):

Social Networks ◽

Community Structure ◽

Community Detection ◽

Linear Time ◽

Deep Understanding ◽

Detection Algorithm ◽

Underlying Structure ◽

Community Detection Algorithm ◽

Stable Community ◽

Synthetic Networks

Social networks are made up of members in society and the social relationships established by the interaction between members. Community structure is an essential attribute of social networks. The question arises that how can we discover the community structure in the network to gain a deep understanding of its underlying structure and mine information from it? In this paper, we introduce a novel community detection algorithm NTCD (Community Detection based on Node Trust). This is a stable community detection algorithm that does not require any parameters settings and has nearly linear time complexity. NTCD determines the community ownership of a node by studying the relationship between the node and its neighbor communities. This relationship is called Node Trust, representing the possibility that the node is in the current community. Node Trust is also a quality function, which is used for community detection by seeking maximum. Experiments on real and synthetic networks show that our algorithm has high accuracy in most data sets and stable community division results. Additionally, through experiments on different types of synthetic networks, we can conclude that our algorithm has good robustness.

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A Novel Overlapping Community Detection Algorithm of Weighted Networks

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.599-601.1369 ◽

2014 ◽

Vol 599-601 ◽

pp. 1369-1373

Author(s):

Huang Bin You ◽

Xue Wu Zhang ◽

Huai Yong Fu ◽

Zhuo Zhang ◽

Min Li ◽

...

Keyword(s):

Community Structure ◽

Community Detection ◽

Classification Accuracy ◽

Time Complexity ◽

Maximum Degree ◽

Detection Algorithm ◽

Overlapping Community Detection ◽

Weighted Networks ◽

Overlapping Community ◽

Community Detection Algorithm

The community structure is a vital property of complex networks. As special networks the weighted networks also have community structure. Nowadays the studies of overlapping community draw attentions of researchers. However, the scale of networks become huge, so it requires the algorithm has lower time complexity and higher classification accuracy. Many existing algorithms cannot meet these two requirements at the same time. So we propose a novel overlapping community detection algorithm. Firstly we apply maximum degree node and its some special adjacent nodes as the initial community, and then expand the initial community by adding eligible nodes to it, finally other communities can be found by repeating these two steps. Experiments results show that our algorithm can detect overlapping community structure from weighted networks successfully, and also reveal that our method has higher division accuracy and lower time complexity than many previously proposed methods.

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Active semisupervised community detection based on asymmetric similarity measure

International Journal of Modern Physics B ◽

10.1142/s0217979215500782 ◽

2015 ◽

Vol 29 (13) ◽

pp. 1550078 ◽

Cited By ~ 3

Author(s):

Mingwei Leng ◽

Liang Huang ◽

Longjie Li ◽

Hanhai Zhou ◽

Jianjun Cheng ◽

...

Keyword(s):

Community Structure ◽

Prior Knowledge ◽

Complex Network ◽

Community Detection ◽

Detection Algorithm ◽

Directed Network ◽

Detection Algorithms ◽

Community Detection Algorithm ◽

Similarity Method ◽

Asymmetric Similarity

Semisupervised community detection algorithms use prior knowledge to improve the performance of discovering the community structure of a complex network. However, getting those prior knowledge is quite expensive and time consuming in many real-world applications. This paper proposes an active semisupervised community detection algorithm based on the similarities between nodes. First, it transforms a given complex network into a weighted directed network based on the proposed asymmetric similarity method, some informative nodes are selected to be the labeled nodes by using an active mechanism. Second, the proposed algorithm discovers the community structure of a complex network by propagating the community labels of labeled nodes to their neighbors based on the similarity between a node and a community. Finally, the performance of the proposed algorithm is evaluated with three real networks and one synthetic network and the experimental results show that the proposed method has a better performance compared with some other community detection algorithms.

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Effectively Detecting Communities by Adjusting Initial Structure via Cores

Complexity ◽

10.1155/2019/9764341 ◽

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.

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A Community Detection Algorithm Based on Node Degree Difference and Node Similarity

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.462-463.458 ◽

2013 ◽

Vol 462-463 ◽

pp. 458-461

Author(s):

Jian Jun Cheng ◽

Peng Fei Wang ◽

Qi Bin Zhang ◽

Zheng Quan Zhang ◽

Ming Wei Leng ◽

...

Keyword(s):

Community Structure ◽

Community Detection ◽

Detection Algorithm ◽

Coarse Grained ◽

Node Degree ◽

Detection Algorithms ◽

Starting Point ◽

Node Similarity ◽

Feasible Algorithm ◽

Community Detection Algorithm

This paper proposes an algorithm called DDSCDA, which is based on the concepts of the node degree difference and the node similarity. In the algorithm, we iteratively extract the node from the network with larger degree and certified the node as a kernel node, then take the kernel node as the founder or initiator of a community to attract its neighbors to join in that community; by doing so, we obtain a partition corresponding to a coarse-grained community structure of the network. Finally taken the coarse-grained community as a starting point, we use the strategy of LPA to propagate labels through the network further. At the end of the algorithm, we obtain the final community structure. We compared the performance with classical community detection algorithms such as LPA, LPAm, FastQ, etc., the experimental results have manifested that our proposal is a feasible algorithm, can extract higher quality communities from the network, and outperforms the previous algorithms significantly.

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Research on Fast Community Detection Algorithm in Microblog

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.513-517.2045 ◽

2014 ◽

Vol 513-517 ◽

pp. 2045-2049

Author(s):

Jie Tian ◽

Hao Guo ◽

Yu Wang

Keyword(s):

Community Structure ◽

Community Detection ◽

Detection Method ◽

Heuristic Method ◽

Computation Time ◽

Detection Algorithm ◽

Core Structure ◽

Large Networks ◽

The Core ◽

Community Detection Algorithm

According to the problem of extracting the community structure of large networks, we propose a simple heuristic method based on community coding optimization. It is shown to outperform the InfoMap community detection method in terms of computation time. Experiments show that our method can find out various communities in microblog, which reveal the core structure of the network.

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