scholarly journals A Graph Clustering Algorithm Using Attraction-Force Similarity for Community Detection

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 13683-13692 ◽  
Author(s):  
Hongfang Zhou ◽  
Bingyan Xi ◽  
Yihui Zhang ◽  
Junhuai Li ◽  
Facun Zhang
Keyword(s):  
Community Detection ◽  
Clustering Algorithm ◽  
Graph Clustering ◽  
Attraction Force

scholarly journals Semantic frame induction through the detection of communities of verbs and their arguments

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Eugénio Ribeiro ◽  
Andreia Sofia Teixeira ◽  
Ricardo Ribeiro ◽  
David Martins de Matos
Keyword(s):  
Community Detection ◽  
Clustering Algorithm ◽  
State Of The Art ◽  
Graph Clustering ◽  
Semantic Role ◽  
Detection Problem ◽  
Current State ◽  
Textual Data ◽  
Semantic Frames ◽  
Do So

Abstract Resources such as FrameNet, which provide sets of semantic frame definitions and annotated textual data that maps into the evoked frames, are important for several NLP tasks. However, they are expensive to build and, consequently, are unavailable for many languages and domains. Thus, approaches able to induce semantic frames in an unsupervised manner are highly valuable. In this paper we approach that task from a network perspective as a community detection problem that targets the identification of groups of verb instances that evoke the same semantic frame and verb arguments that play the same semantic role. To do so, we apply a graph-clustering algorithm to a graph with contextualized representations of verb instances or arguments as nodes connected by edges if the distance between them is below a threshold that defines the granularity of the induced frames. By applying this approach to the benchmark dataset defined in the context of SemEval 2019, we outperformed all of the previous approaches to the task, achieving the current state-of-the-art performance.

scholarly journals Community Detection Based on Graph Representation Learning in Evolutionary Networks

2021 ◽  
Vol 11 (10) ◽  
pp. 4497
Author(s):  
Dongming Chen ◽  
Mingshuo Nie ◽  
Jie Wang ◽  
Yun Kong ◽  
Dongqi Wang ◽  
...  
Keyword(s):  
Community Detection ◽  
Network Structure ◽  
Clustering Algorithm ◽  
Laplacian Matrix ◽  
Representation Learning ◽  
Detection Algorithm ◽  
Graph Representation ◽  
Time Slice ◽  
Current Time ◽  
Evolutionary Networks

Aiming at analyzing the temporal structures in evolutionary networks, we propose a community detection algorithm based on graph representation learning. The proposed algorithm employs a Laplacian matrix to obtain the node relationship information of the directly connected edges of the network structure at the previous time slice, the deep sparse autoencoder learns to represent the network structure under the current time slice, and the K-means clustering algorithm is used to partition the low-dimensional feature matrix of the network structure under the current time slice into communities. Experiments on three real datasets show that the proposed algorithm outperformed the baselines regarding effectiveness and feasibility.

Efficient Vector Partitioning Algorithms for Graph Clustering

2020 ◽  
Vol 1 (2) ◽  
pp. 101-123
Author(s):  
Hiroaki Shiokawa ◽  
Yasunori Futamura
Keyword(s):  
Social Networks ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Ground Truth ◽  
Graph Clustering ◽  
Mining Communities ◽  
Fine Grained ◽  
Efficient Vector ◽  
Public Datasets ◽  
Many Core

This paper addressed the problem of finding clusters included in graph-structured data such as Web graphs, social networks, and others. Graph clustering is one of the fundamental techniques for understanding structures present in the complex graphs such as Web pages, social networks, and others. In the Web and data mining communities, the modularity-based graph clustering algorithm is successfully used in many applications. However, it is difficult for the modularity-based methods to find fine-grained clusters hidden in large-scale graphs; the methods fail to reproduce the ground truth. In this paper, we present a novel modularity-based algorithm, \textit{CAV}, that shows better clustering results than the traditional algorithm. The proposed algorithm employs a cohesiveness-aware vector partitioning into the graph spectral analysis to improve the clustering accuracy. Additionally, this paper also presents a novel efficient algorithm \textit{P-CAV} for further improving the clustering speed of CAV; P-CAV is an extension of CAV that utilizes the thread-based parallelization on a many-core CPU. Our extensive experiments on synthetic and public datasets demonstrate the performance superiority of our approaches over the state-of-the-art approaches.

scholarly journals Implementasi Graph Clustering Algorithm Modification Maximum Standard Deviation Reduction (MMSDR) dalam Clustering Provinsi di Indonesia Menurut Indikator Kesejahteraan Rakyat

Faktor Exacta ◽  
2020 ◽  
Vol 13 (2) ◽  
pp. 73
Author(s):  
Nurfidah Dwitiyanti ◽  
Septian Wulandari ◽  
Noni Selvia
Keyword(s):  
Economic Growth ◽  
Standard Deviation ◽  
Clustering Algorithm ◽  
Graph Clustering ◽  
Public Welfare ◽  
Poor People ◽  
Equitable Distribution ◽  
Maximum Standard Deviation ◽  
Central Statistics

<p>The population of Indonesia from year to year has increased. The increase in population must also be accompanied by increased economic growth in Indonesia. The increase in economic growth in Indonesia is marked by the reduction in the number of poor people in Indonesia. In addition, the increase in economic growth is reflected in the equitable distribution of public income in the country. Even though there are still many Indonesian people who are not yet prosperous in economic terms. To overcome, it is necessary to have clustering and characteristics of 34 provinces in Indonesia by implementing the Modification Maximum Standard Deviation Reduction (MMSDR) graph clustering algorithm. The data used are indicators of public welfare in 2017 obtained from the Central Statistics Agency. There are 9 indicators of community welfare used in this research. There are four stages in the MMSDR algorithm namely the "MST", "Subdivide", "Biggest Stepping" and "Create Clusters" processes. The results of this study can be seen from the distance between the nodes or between one province and another province produced 22 clusters. From the cluster results obtained using the MMSDR algorithm on welfare data, there are many clusters formed with cluster members formed at most two nodes (province).</p><p> Keywords: MMSDR, Clustering, Welfare of People</p>

scholarly journals An Improved Spectral Clustering Community Detection Algorithm Based on Probability Matrix

2020 ◽  
Vol 2020 ◽  
pp. 1-6
Author(s):  
Shuxia Ren ◽  
Shubo Zhang ◽  
Tao Wu
Keyword(s):  
Community Detection ◽  
Spectral Clustering ◽  
Clustering Algorithm ◽  
Transition Probability ◽  
Clustering Algorithms ◽  
Detection Algorithm ◽  
Community Information ◽  
The Mean ◽  
Community Detection Algorithm ◽  
Spectral Clustering Algorithm

The similarity graphs of most spectral clustering algorithms carry lots of wrong community information. In this paper, we propose a probability matrix and a novel improved spectral clustering algorithm based on the probability matrix for community detection. First, the Markov chain is used to calculate the transition probability between nodes, and the probability matrix is constructed by the transition probability. Then, the similarity graph is constructed with the mean probability matrix. Finally, community detection is achieved by optimizing the NCut objective function. The proposed algorithm is compared with SC, WT, FG, FluidC, and SCRW on artificial networks and real networks. Experimental results show that the proposed algorithm can detect communities more accurately and has better clustering performance.

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