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Improved relation extraction through key phrase identification using community detection on dependency trees

COMPUTER SPEECH AND LANGUAGE 2025年 89卷

作者： Liu, Shuang Chen, Xunqin Meng, Jiana Lukac, Niko Dalian Minzu Univ Sch Comp Sci & Engn Dalian 116600 Peoples R China Univ Maribor Fac Elect Engn & Comp Sci SI-2000 Maribor Slovenia

A method for extracting relations from sentences by utilizing their dependency trees to identify key phrases is presented in this paper. Dependency trees are commonly used in natural language processing to represent the grammatical structure of a sentence, and this approach builds upon this representation to extract meaningful relations between phrases. Identifying key phrases is crucial in relation extraction as they often indicate the entities and actions involved in a relation. The method uses community detection algorithms on the dependency tree to identify groups of related words that form key phrases, such as subject-verb-object structures. The experiments on the Semeval-2010 task8 dataset and the TACRED dataset demonstrate that the proposed method outperforms existing baseline methods.

关键词： community detection algorithms Dependency trees Relation extraction

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Efficient Parallel Algorithm for Approximating Betweenness Centrality Values of Top k Nodes in Large Graphs

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2025年第4-5期37卷

作者： Toroslu, Ismail H. Suleymanli, Gadir Middle East Tech Univ Comp Engn Ankara Turkiye Univ Ghent Comp Sci Ghent East Flanders Belgium

Computing betweenness centrality (BC) in large graphs is crucial for various applications, including telecommunications, social, and biological networks. However, the huge size of the data presents significant challenges. In this paper, we introduce a novel approximate approach for efficiently extracting top k BC nodes by combining the Louvain community detection algorithm with Brandes' algorithm. Our method significantly enhances the runtime efficiency of the traditional Brandes' algorithm while preserving accuracy across both synthetic and real-world datasets. Additionally, our approach is suitable for parallelization, further improving its efficiency. Experimental results confirm the effectiveness of our method for large and sparse graphs.

关键词： betweenness centrality community detection algorithms heuristics Louvain clustering multiprocessing parallelization

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An upper approximation based community detection algorithm for complex networks

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DECISION SUPPORT SYSTEMS 2017年 96卷 103-118页

作者： Kumar, Pradeep Gupta, Samtat Bhasker, Bharat Indian Inst Management Lucknow IT & Syst Dept Lucknow Uttar Pradesh India

The emergence of multifarious complex networks has attracted researchers and practitioners from various disciplines. Discovering cohesive subgroups or communities in complex networks is essential to understand the dynamics of real-world systems. Researchers have made persistent efforts to investigate and infer community patterns in complex networks. However, real-world networks exhibit various characteristics wherein existing communities are not only disjoint but are also overlapping and nested. The existing literature on community detection consists of limited methods to discover co-occurring disjoint, overlapping and nested communities. In this work, we propose a novel rough set based algorithm capable of uncovering true community structure in networks, be it disjoint overlapping or nested. Initial sets of granules are constructed using neighborhood connectivity around the nodes and represented as rough sets. Subsequently, we iteratively obtain the constrained connectedness upper approximation of these sets. To constrain the sets and merge them during each iteration, we utilize the concept of relative connectedness among the nodes. We illustrate the proposed algorithm on a toy network and evaluate it on fourteen real-world benchmark networks. Experimental results show that the proposed algorithm reveals more accurate communities and significantly outperforms state-of-the-art techniques. (C) 2017 Elsevier B.V. All rights reserved.

关键词： community structure Complex networks community detection algorithms Overlapping communities Neighborhood model Rough sets

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Overlapping community detection using neighborhood ratio matrix

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2015年 421卷 510-521页

作者： Eustace, Justine Wang, Xingyuan Cui, Yaozu Dalian Univ Technol Fac Elect Informat & Elect Engn Dalian 116024 Peoples R China

The participation of a node in more than one community is a common phenomenon in complex networks. However most existing methods, fail to identify nodes with multiple community affiliation, correctly. In this paper, a unique method to define overlapping community in complex networks is proposed, using the overlapping neighborhood ratio to represent relations between nodes. Matrix factorization is then utilized to assign nodes into their corresponding community structures. Moreover, the proposed method demonstrates the use of Perron clusters to estimate the number of overlapping communities in a network. Experimental results in real and artificial networks show, with great accuracy, that the proposed method succeeds to recover most of the overlapping communities existing in the network. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Neighboring ratio Complex networks community detection algorithms Data mining Peron clusters

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KO: Modularity optimization in community detection

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NEURAL COMPUTING & APPLICATIONS 2023年第15期35卷 11073-11087页

作者： Oztemiz, Furkan Karci, Ali Inonu Univ Dept Software Engn TR-44210 Malatya Turkiye

Many algorithms have been developed to detect communities in networks. The success of these developed algorithms varies according to the types of networks. A community detection algorithm cannot always guarantee the best results on all networks. The most important reason for this is the approach algorithms follow when dividing any network into communities (sub-networks). The modularity of the network determines the quality of communities in networks. It is concluded that networks with high modularity values are divided into more successful communities (clusters, sub-networks). This study proposes a modularity optimization algorithm to increase clustering success in any network without being dependent on any community detection algorithm. The basic approach of the proposed algorithm is to transfer nodes at the community boundary to neighboring communities if they meet the specified conditions. The method called KO (Karci-Oztemiz) optimization algorithm maximizes the modularity value of any community detection algorithm in the best case, while it does not change the modularity value in the worst case. For the KO algorithm's test, in this study, Walktrap, Cluster Edge Betweenness, Label Propagation, Fast Greedy, and Leading Eigenvector community detection algorithms have been applied on three popular networks that were unweighted and undirected previously used in the literature. The community structures created by five community detection algorithms were optimized via the KO algorithm and the success of the proposed method was analyzed. When the results are examined, the modularity values of the community detection algorithms applied on the three different networks have increased at varying rates (0%,.,14.73%).

关键词： community modularity community detection algorithms Graph network Modularity optimization

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Segmentation of large images based on super-pixels and community detection in graphs

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IET IMAGE PROCESSING 2017年第12期11卷 1219-1228页

作者： Linares, Oscar A. C. Botelho, Glenda Michele Rodrigues, Francisco Aparecido Batista Neto, Joao Univ Sao Paulo Inst Ciencias Matemat & Comp Campus Sao CarlosCaixa Postal 668 BR-13560970 Sao Carlos SP Brazil Univ Fed Tocantins Palmas Brazil

Image segmentation has many applications which range from machine learning to medical diagnosis. In this study, the authors propose a framework for the segmentation of images based on super-pixels and algorithms for community identification in graphs. The super-pixel pre-segmentation step reduces the number of nodes in the graph, rendering the method the ability to process large images. Moreover, community detection algorithms provide more accurate segmentation than traditional approaches based on spectral graph partition. The authors also compared their method with two algorithms: (i) the graph-based approach by Felzenszwalb and Huttenlocher and (ii) the contour-based method by Arbelaez. Results have shown that their method provides more precise segmentation and is faster than both of them.

关键词： image segmentation graph theory object detection contour-based method graph-based approach spectral graph partition community detection algorithms super-pixel pre-segmentation step medical diagnosis machine learning image segmentation

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community detection using local neighborhood in complex networks

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2015年 436卷 665-677页

作者： Eustace, Justine Wang, Xingyuan Cui, Yaozu Dalian Univ Technol Fac Elect Informat & Elect Engn Dalian 116024 Peoples R China

It is common to characterize community structure in complex networks using local neighborhood. Existing related methods fail to estimate the accurate number of nodes present in each community in the network. In this paper a community detection algorithm using local community neighborhood ratio function is proposed. The proposed algorithm predicts vertex association to a specific community using visited node overlapped neighbors. In the beginning, the algorithm detects local communities;then through iterations and local neighborhood ratio function, final communities are detected by merging close related local communities. Analysis of simulation results on real and artificial networks shows the proposed algorithm detects well defined communities in both networks by wide margin. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Complex networks Data mining community detection algorithms Neighborhood ratio Behavior science

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Mapping cortical functions with a local community detection algorithm

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JOURNAL OF COMPLEX NETWORKS 2014年第4期2卷 637-653页

作者： Gronchi, Giorgio Guazzini, Andrea Massaro, Emanuele Bagnoli, Franco Univ Florence Dept Hlth Sci Florence Italy Univ Florence Dept Educ & Psychol Florence Italy Univ Florence CSDC Florence Italy Univ Florence Dept Informat Engn Florence Italy Carnegie Mellon Univ Dept Civil & Environm Engn Pittsburgh PA 15213 USA US Army Corps Engineer Risk & Decis Sci Team Concord MA 01742 USA Univ Florence Dept Phys & Astron Florence Italy Ist Nazl Fis Nucl Sez Firenze Florence Italy

With the availability of detailed information about the connectivity among brain areas, network analysis tools have been applied to datasets comprising cat and macaque cortical networks. Using evolutionary optimization algorithms (based on global analysis of the network), similarities between anatomical connections and functional properties of the areas of the brain have been found in previous works. To find functional properties of the brain, we have applied NetExplorer, a novel community detection algorithm based on local information dynamics, to two different datasets of cortical connectivity. We have compared the performances of NetExplorer with the results obtained by other six algorithms (Infomap, Hierarchical Infomap, Louvain, Modularity Optimization, Label Propagation and Oslom) with the same two datasets of cortical connectivity. In both datasets, NetExplorer places among the best algorithms in predicting the actual functional cortical clusters. We discuss the algorithm results considering the misattributions of brain areas to the different clusters and emphasizing the connections that are explainable by their multimodal functions.

关键词： graph theory community detection algorithms corticocortical connections

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Artificial Benchmark for community detection (ABCD)-Fast random graph model with community structure

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NETWORK SCIENCE 2021年第2期9卷 153-178页

作者： Kaminski, Bogumil Pralat, Pawel Theberge, Francois SGH Warsaw Sch Econ Decis Anal & Support Unit Warsaw Poland Ryerson Univ Dept Math Toronto ON Canada Tutte Inst Math & Comp Ottawa ON Canada

Most of the current complex networks that are of interest to practitioners possess a certain community structure that plays an important role in understanding the properties of these networks. For instance, a closely connected social communities exhibit faster rate of transmission of information in comparison to loosely connected communities. Moreover, many machine learning algorithms and tools that are developed for complex networks try to take advantage of the existence of communities to improve their performance or speed. As a result, there are many competing algorithms for detecting communities in large networks. Unfortunately, these algorithms are often quite sensitive and so they cannot be fine-tuned for a given, but a constantly changing, real-world network at hand. It is therefore important to test these algorithms for various scenarios that can only be done using synthetic graphs that have built-in community structure, power law degree distribution, and other typical properties observed in complex networks. The standard and extensively used method for generating artificial networks is the LFR graph generator. Unfortunately, this model has some scalability limitations and it is challenging to analyze it theoretically. Finally, the mixing parameter mu, the main parameter of the model guiding the strength of the communities, has a non-obvious interpretation and so can lead to unnaturally defined networks. In this paper, we provide an alternative random graph model with community structure and power law distribution for both degrees and community sizes, the Artificial Benchmark for community detection (ABCD graph). The model generates graphs with similar properties as the LFR one, and its main parameter xi can be tuned to mimic its counterpart in the LFR model, the mixing parameter mu. We show that the new model solves the three issues identified above and more. In particular, we test the speed of our algorithm and do a number of experiments comparing basic pro

关键词： community detection algorithms synthetic models random graphs

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Comparative Analysis of community detection Methods for Online Learning Environments

Comparative Analysis of Community Detection Methods for Onli...

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2024 International Conference on Computing, Communication, Cybersecurity and AI

作者： Sakle, Mukesh Prajapat, Shaligram Shri GS Inst Technol & Sci Indore India Devi Ahilya Univ Int Inst Profess Studies Indore India

ISBN: (纸本)9783031744426;9783031744433

The understanding, analysis, and prediction of the behaviours and dynamics of networks associated with several disciplines in sociology, criminology, biology, medicine, communication, economics, and academics have advanced significantly because of the discovery of community structure. Finding communities and grouping them together is a useful first step in figuring out the behavioural patterns and structural characteristics of social networks. Many educational approaches have recently gradually embraced online learning, which raises several concerns regarding how to evaluate students' participation, teamwork, and behaviours in the brand-new, emergent learning communities. In this research work we have applied five algorithms of community detection on three different data set related to social network and online learning environment. The purpose of this work is to evaluate how community detection techniques are applied to network structure analysis in online learning environments. Experimental results indicate that the Girvan-newman and Louvain algorithm are giving the better results for community detection.

关键词： Social Networks community Clustering Modularity community detection algorithms Social Network Analysis (SNA) Online learning Environments

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