scholarly journals Overlapping Community Discovery Method Based on Two Expansions of Seeds

Symmetry ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 18
Author(s):  
Yan Li ◽  
Jing He ◽  
Youxi Wu ◽  
Rongjie Lv
Keyword(s):  
Symmetric Matrix ◽  
Fitness Function ◽  
Local Maximum ◽  
Community Discovery ◽  
Network Nodes ◽  
Overlapping Community ◽  
The Common ◽  
New Research ◽  
Discovery Method ◽  
Discovery Algorithms

The real world can be characterized as a complex network sto in symmetric matrix. Community discovery (or community detection) can effectively reveal the common features of network groups. The communities are overlapping since, in fact, one thing often belongs to multiple categories. Hence, overlapping community discovery has become a new research hotspot. Since the results of the existing community discovery algorithms are not robust enough, this paper proposes an effective algorithm, named Two Expansions of Seeds (TES). TES adopts the topological feature of network nodes to find the local maximum nodes as the seeds which are based on the gravitational degree, which makes the community discovery robust. Then, the seeds are expanded by the greedy strategy based on the fitness function, and the community cleaning strategy is employed to avoid the nodes with negative fitness so as to improve the accuracy of community discovery. After that, the gravitational degree is used to expand the communities for the second time. Thus, all nodes in the network belong to at least one community. Finally, we calculate the distance between the communities and merge similar communities to obtain a less- undant community structure. Experimental results demonstrate that our algorithm outperforms other state-of-the-art algorithms.

Community Discovery Method in Networks Based on Topological Potential

2009 ◽  
Vol 20 (8) ◽  
pp. 2241-2254 ◽  
Author(s):  
Wen-Yan GAN ◽  
Nan HE ◽  
De-Yi LI ◽  
Jian-Min WANG
Keyword(s):  
Community Discovery ◽  
Discovery Method

scholarly journals Multityped Community Discovery in Time-Evolving Heterogeneous Information Networks Based on Tensor Decomposition

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Jibing Wu ◽  
Lianfei Yu ◽  
Qun Zhang ◽  
Peiteng Shi ◽  
Lihua Liu ◽  
...  
Keyword(s):  
Real World ◽  
Tensor Decomposition ◽  
Information Networks ◽  
Community Discovery ◽  
Star Network ◽  
Heterogeneous Information ◽  
Heterogeneous Information Networks ◽  
General Network ◽  
Real World Datasets ◽  
Discovery Method

The heterogeneous information networks are omnipresent in real-world applications, which consist of multiple types of objects with various rich semantic meaningful links among them. Community discovery is an effective method to extract the hidden structures in networks. Usually, heterogeneous information networks are time-evolving, whose objects and links are dynamic and varying gradually. In such time-evolving heterogeneous information networks, community discovery is a challenging topic and quite more difficult than that in traditional static homogeneous information networks. In contrast to communities in traditional approaches, which only contain one type of objects and links, communities in heterogeneous information networks contain multiple types of dynamic objects and links. Recently, some studies focus on dynamic heterogeneous information networks and achieve some satisfactory results. However, they assume that heterogeneous information networks usually follow some simple schemas, such as bityped network and star network schema. In this paper, we propose a multityped community discovery method for time-evolving heterogeneous information networks with general network schemas. A tensor decomposition framework, which integrates tensor CP factorization with a temporal evolution regularization term, is designed to model the multityped communities and address their evolution. Experimental results on both synthetic and real-world datasets demonstrate the efficiency of our framework.

scholarly journals Eigenvalue Based Approach for Global Consensus in Multiagent Systems with Nonlinear Dynamics

2014 ◽  
Vol 2014 ◽  
pp. 1-6
Author(s):  
Wei Qian ◽  
Lei Wang
Keyword(s):  
Nonlinear Dynamics ◽  
Multiagent Systems ◽  
Multiagent System ◽  
Symmetric Matrix ◽  
Algebraic Connectivity ◽  
Communication Graph ◽  
Consensus Protocol ◽  
Global Consensus ◽  
The Common ◽  
Analytical Result

This paper addresses the global consensus of nonlinear multiagent systems with asymmetrically coupled identical agents. By employing a Lyapunov function and graph theory, a sufficient condition is presented for the global exponential consensus of the multiagent system. The analytical result shows that, for a weakly connected communication graph, the algebraic connectivity of a redefined symmetric matrix associated with the directed graph is used to evaluate the global consensus of the multiagent system with nonlinear dynamics under the common linear consensus protocol. The presented condition is quite simple and easily verified, which can be effectively used to design consensus protocols of various weighted and directed communications. A numerical simulation is also given to show the effectiveness of the analytical result.

scholarly journals X-Mark: a benchmark for node-attributed community discovery algorithms

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Salvatore Citraro ◽  
Giulio Rossetti
Keyword(s):  
State Of The Art ◽  
Graph Clustering ◽  
Clustering Methods ◽  
Community Discovery ◽  
Attributed Graphs ◽  
Node Labels ◽  
Discovery Algorithms ◽  
Shed Light

AbstractGrouping well-connected nodes that also result in label-homogeneous clusters is a task often known as attribute-aware community discovery. While approaching node-enriched graph clustering methods, rigorous tools need to be developed for evaluating the quality of the resulting partitions. In this work, we present X-Mark, a model that generates synthetic node-attributed graphs with planted communities. Its novelty consists in forming communities and node labels contextually while handling categorical or continuous attributive information. Moreover, we propose a comparison between attribute-aware algorithms, testing them against our benchmark. Accordingly to different classification schema from recent state-of-the-art surveys, our results suggest that X-Mark can shed light on the differences between several families of algorithms.

COMPARING MINIMUM NEIGHBORHOOD EVALUATION SCHEMES FOR FINDING SPATIALLY ROBUST SOLUTIONS

2007 ◽  
Vol 17 (03) ◽  
pp. 299-309
Author(s):  
GRZEGORZ DRZADZEWSKI ◽  
MARK WINEBERG
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Operational Definition ◽  
Significant Drop ◽  
Robust Solutions ◽  
Common Definition ◽  
Solution Robustness ◽  
The Common

The common definition for robust solutions considers a solution robust if it remains optimal when the parameters defining the fitness function are perturbed. A second definition that can be found in the literature: robustness occurs when a solution can be varied spatially without a significant drop in fitness. We propose an alternative operational definition for spatial robustness: both the solution and the neighbourhood around the solution has fitness above a given threshold. With this new definition, we created a set of functions with useful properties to allow for the testing of solution robustness. The performance of a Genetic Algorithm (GA) is then evaluated based on its ability to identify multiple robust solutions based on the above robustness definition. Different neighbourhood evaluation schemes are identified from the literature and compared, with the minimum neighbour technique proving to be the most effective.

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