Incremental Community Detection on Large Complex Attributed Network

2021 ◽  
Vol 15 (6) ◽  
pp. 1-20
Author(s):  
Zhe Chen ◽  
Aixin Sun ◽  
Xiaokui Xiao
Keyword(s):  
Community Detection ◽  
Large Scale ◽  
Network Data ◽  
Topological Information ◽  
Community Membership ◽  
Attributed Network ◽  
Benchmark Datasets ◽  
Modularity Maximization ◽  
Large Scale Networks

Community detection on network data is a fundamental task, and has many applications in industry. Network data in industry can be very large, with incomplete and complex attributes, and more importantly, growing. This calls for a community detection technique that is able to handle both attribute and topological information on large scale networks, and also is incremental. In this article, we propose inc-AGGMMR, an incremental community detection framework that is able to effectively address the challenges that come from scalability, mixed attributes, incomplete values, and evolving of the network. Through construction of augmented graph, we map attributes into the network by introducing attribute centers and belongingness edges. The communities are then detected by modularity maximization. During this process, we adjust the weights of belongingness edges to balance the contribution between attribute and topological information to the detection of communities. The weight adjustment mechanism enables incremental updates of community membership of all vertices. We evaluate inc-AGGMMR on five benchmark datasets against eight strong baselines. We also provide a case study to incrementally detect communities on a PayPal payment network which contains users with transactions. The results demonstrate inc-AGGMMR’s effectiveness and practicability.

A new spectral coarse-graining algorithm based on K-means clustering in complex networks

2019 ◽  
Vol 33 (01) ◽  
pp. 1850421 ◽  
Author(s):  
Lang Zeng ◽  
Zhen Jia ◽  
Yingying Wang
Keyword(s):  
Complex Networks ◽  
Large Scale ◽  
Dynamic Properties ◽  
Coarse Graining ◽  
Kuramoto Model ◽  
Coarse Grained ◽  
Original Network ◽  
Topological Information ◽  
Real World Applications ◽  
Large Scale Networks

Coarse-graining of complex networks is one of the important algorithms to study large-scale networks, which is committed to reducing the size of networks while preserving some topological information or dynamic properties of the original networks. Spectral coarse-graining (SCG) is one of the typical coarse-graining algorithms, which can keep the synchronization ability of the original network well. However, the calculation of SCG is large, which limits its real-world applications. And it is difficult to accurately control the scale of the coarse-grained network. In this paper, a new SCG algorithm based on K-means clustering (KCSCG) is proposed, which cannot only reduce the amount of calculation, but also accurately control the size of coarse-grained network. At the same time, KCSCG algorithm has better effect in keeping the network synchronization ability than SCG algorithm. A large number of numerical simulations and Kuramoto-model example on several typical networks verify the feasibility and effectiveness of the proposed algorithm.

scholarly journals Strategies for Monitoring Multiuse Trail Networks

2017 ◽  
Vol 2644 (1) ◽  
pp. 100-110 ◽  
Author(s):  
Greg Lindsey ◽  
Jeffrey S. Wilson ◽  
Jueyu Wang ◽  
Tracy Hadden-Loh
Keyword(s):  
Nonprofit Organizations ◽  
Large Scale ◽  
Low Cost ◽  
Facility Planning ◽  
Monitoring Networks ◽  
Site Specific ◽  
New Challenges ◽  
Large Scale Networks ◽  
Selection Of

Many municipalities, park districts, and nonprofit organizations have begun monitoring nonmotorized traffic on multiuse trails as the need for information about the use of facilities has grown and relatively low-cost sensors for automated monitoring have become available. As they have gained experience, they have begun to move from site-specific monitoring on individual trails to a more comprehensive monitoring of trail networks. This case study review compares strategies developed by 10 organizations for monitoring traffic on multiuse trails, including local, multicounty, statewide, and multistate trail networks. The focus is on approaches to the design of monitoring networks, particularly the rationales or objectives for monitoring and the selection of monitoring sites. It is shown that jurisdictions are following principles of monitoring established by FHWA and that the design of monitoring networks is evolving to meet new challenges, including monitoring large-scale networks. Relevant outcomes and implications for practice are summarized. The researchers concluded that FHWA guidelines can be adapted to many circumstances and can increase information for decision making. Trail monitoring is informing decisions related to facility planning, investment, and safety.

scholarly journals Performance Evaluation of Modularity Based Community Detection Algorithms in Large Scale Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-15 ◽  
Author(s):  
Vinícius da Fonseca Vieira ◽  
Carolina Ribeiro Xavier ◽  
Nelson Francisco Favilla Ebecken ◽  
Alexandre Gonçalves Evsukoff
Keyword(s):  
Spectral Method ◽  
Community Detection ◽  
High Performance ◽  
Large Scale ◽  
Fine Tuning ◽  
Detection Algorithms ◽  
Research Areas ◽  
Structure Detection ◽  
Wide Range ◽  
Large Scale Networks

Community structure detection is one of the major research areas of network science and it is particularly useful for large real networks applications. This work presents a deep study of the most discussed algorithms for community detection based on modularity measure: Newman’s spectral method using a fine-tuning stage and the method of Clauset, Newman, and Moore (CNM) with its variants. The computational complexity of the algorithms is analysed for the development of a high performance code to accelerate the execution of these algorithms without compromising the quality of the results, according to the modularity measure. The implemented code allows the generation of partitions with modularity values consistent with the literature and it overcomes 1 million nodes with Newman’s spectral method. The code was applied to a wide range of real networks and the performances of the algorithms are evaluated.

scholarly journals A Node Influence Based Label Propagation Algorithm for Community Detection in Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Yan Xing ◽  
Fanrong Meng ◽  
Yong Zhou ◽  
Mu Zhu ◽  
Mengyu Shi ◽  
...  
Keyword(s):  
Community Detection ◽  
Large Scale ◽  
Detection Method ◽  
Label Propagation ◽  
Experimental Results ◽  
Superior Performance ◽  
Propagation Algorithm ◽  
Large Scale Networks ◽  
Poor Stability

Label propagation algorithm (LPA) is an extremely fast community detection method and is widely used in large scale networks. In spite of the advantages of LPA, the issue of its poor stability has not yet been well addressed. We propose a novel node influence based label propagation algorithm for community detection (NIBLPA), which improves the performance of LPA by improving the node orders of label updating and the mechanism of label choosing when more than one label is contained by the maximum number of nodes. NIBLPA can get more stable results than LPA since it avoids the complete randomness of LPA. The experimental results on both synthetic and real networks demonstrate that NIBLPA maintains the efficiency of the traditional LPA algorithm, and, at the same time, it has a superior performance to some representative methods.

scholarly journals Research on Community Detection in Complex Networks Based on Internode Attraction

Entropy ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. 1383
Author(s):  
Jinfang Sheng ◽  
Cheng Liu ◽  
Long Chen ◽  
Bin Wang ◽  
Junkai Zhang
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Community Detection ◽  
Large Scale ◽  
Rapid Development ◽  
Structure Detection ◽  
Detection Approach ◽  
Real World Datasets ◽  
New Community ◽  
Large Scale Networks

With the rapid development of computer technology, the research on complex networks has attracted more and more attention. At present, the research directions of cloud computing, big data, internet of vehicles, and distributed systems with very high attention are all based on complex networks. Community structure detection is a very important and meaningful research hotspot in complex networks. It is a difficult task to quickly and accurately divide the community structure and run it on large-scale networks. In this paper, we put forward a new community detection approach based on internode attraction, named IACD. This algorithm starts from the perspective of the important nodes of the complex network and refers to the gravitational relationship between two objects in physics to represent the forces between nodes in the network dataset, and then perform community detection. Through experiments on a large number of real-world datasets and synthetic networks, it is shown that the IACD algorithm can quickly and accurately divide the community structure, and it is superior to some classic algorithms and recently proposed algorithms.

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