Community Partition Algorithm Research Articles

A multi-node attack scheme based on community partitioning in large scale infrastructure networks

Node attack plays a vital role in evaluating the reliability of large scale infrastructure networks, which has attracted intensive research interests. Unfortunately, most of them focus on single node attack. Multi-node attack based on MBA (Module based Adaptive) is a promising research vector with great potential in improving efficiency. However, MBA-based node attack typically needs to repetitively consider community structures of large networks during the entire node attack process, which brings significant loss in network reliability and causes computational overheads. In light of this, we propose a novel scheme based on MBA to implement multi-node attack, which adopts the Multilevel community partitioning algorithm to overcome the above limitations. We evaluate our scheme on two artificial synthetic networks and four real-world networks with comparison to four widely used node attack schemes, and demonstrate the non-negligible superiority of our scheme over all benchmarks. Especially in the US Powergrid, our scheme can always achieve robustness around 10 times that of other schemes when deleting the same percentage of nodes. To the best of our knowledge, we are the first to achieve an MBA-based node attack scheme that only needs to execute the community partitioning algorithm once during the entire node deletion process.

A community partitioning algorithm for cyberspace

Community partitioning is an effective technique for cyberspace mapping. However, existing community partitioning algorithm only uses the topological structure of the network to divide the community and disregards factors such as real hierarchy, overlap, and directionality of information transmission between communities in cyberspace. Consequently, the traditional community division algorithm is not suitable for dividing cyberspace resources effectively. Based on cyberspace community structure characteristics, this study introduces an algorithm that combines an improved local fitness maximization (LFM) algorithm with the PageRank (PR) algorithm for community partitioning on cyberspace resources, called PR-LFM. First, seed nodes are determined using degree centrality, followed by local community expansion. Nodes belonging to multiple communities undergo further partitioning so that they are retained in the community where they are most important, thus preserving the community’s original structure. The experimental data demonstrate good results in the resource division of cyberspace.

Judgment of Social Governance System Risk Based on Big Data Analysis

Accurate risk identification, scientific risk assessment, effective risk early warning, and real-time risk monitoring will all benefit from the advancement of data science and the development of the data industry. Simultaneously, social risk prevention and control face numerous obstacles in the growth of information technology, data integration and mining, data information disclosure, and data culture popularization in the era of big data. It can be improved in terms of risk prevention and control concept innovation. In addition, it can also be improved in terms of network public opinion guidance, control mechanism improvement, social security prevention, control system optimization, and the introduction and training of big data talent to promote scientific and accurate social risk prevention and control. As society moves into the 5.0 era, many social risks rise and new features emerge, posing larger threats and unprecedented challenges to social risk governance. Big data, as a result of the emerging technology revolution, offers a wealth of governance resources and brand-new governance concepts that traditional governance mechanisms lack, offering technical assistance. This article presents a parallel community partition algorithm using MapReduce to analyze big data. The proposed model is established for the big data thinking paradigm to promote social risk governance innovation. The proposed model supports the top-level model and does inclusive forecasting for social risk management that supports resource integration. The proposed model also realizes diversified and coordinated risk governance. The results reveal the significance of the proposed model.

A Software Vulnerability Detection Method Based on Complex Network Community

To find out whether there is any vulnerability in software programs where conditional judgment is ignored, this article proposes a software vulnerability detection method based on complex network community. First, the method abstracts the software system into a directed weighted graph by using the software algebraic component model and then preprocesses the directed weighted graph to get a complex network graph. Then, by using the partition algorithm, the complex network graph is divided into the communities, and the key nodes in communities are found by nRank algorithm. Finally, the graph of the key nodes with high influence is matched with the complex network graph that has been preprocessed. In order to evaluate the effectiveness of the community partition algorithm and the nRank algorithm, comparative experiments are carried out on two datasets. The experimental results show that the community partition algorithm is better than the comparison algorithm in precision, recall, and comprehensive evaluation index, and the nRank algorithm is closer to the result of degree centrality measurement index than the PageRank algorithm and the LeaderRank algorithm. The spring-shiro-training project is used to verify the vulnerability detection method based on complex network community, and the results show that the method is effective.

A community partitioning algorithm based on network enhancement

In recent years, as an effective method to mine information from the complex network, community discovery has been widely used in social network, financial risk control and other fields. However, the existing community discovery algorithms are not effective in dealing with complex network which always contains fuzzy community structure. With the help of graph convolution, the proposed algorithm defines the connectivity between any nodes in a network and constructs the symmetric doubly stochastic matrix. Then, the algorithm enhances the network by the nonlinear transformation of the eigenvalues of the symmetric doubly stochastic matrix and makes the original fuzzy community structure become clear. Experimental results show that this method can effectively sharpen the community structure of a network and improve the effect of community partitioning.

Opinion leader detection using whale optimization algorithm in online social network

In the current digital era, optimization is one of the most significant problems in the social network. Most of the issues related to optimization are NP-complete and not possible to solve them in polynomial time. Detection of opinion leader based on their optimized centrality measure is a critical issue. The opinion leaders have a non-trivial influence on the other user's decision-making process and can solve various problems related to the diffusion of new products and innovations in the real world. In this paper, we proposed a new Social Network-based Whale Optimization Algorithm (SNWOA) to find the top-N opinion leaders by measuring the reputation of the user using various standard optimization function in the network. The proposed algorithm is advantageous to determine the opinion leaders because it based on the bubble-net hunting behavior of humpback whales. The algorithm found the best possible solution as the number of users raises progressively in the network; therefore, the general complexity of the algorithm remains unchanged. Besides, we also proposed a new approach to categorize the communities based on the similarity index comprising neighbor similarity and clustering coefficient as the significant components. Initially, we computed the objective function of each user by using their centralities and deployed the proposed algorithm with different optimization functions to identify the local and universal opinion leaders. We implemented the proposed algorithm on the real and synthesized datasets and compared the result based on the accuracy, precision, recall, and F1-score. The result indicates that the proposed algorithms give a better result as compared to the other standard Social Network Analysis (SNA) measures. We also concluded that the community partitioning algorithm is even better than the other community detection algorithms based on different parameters and computational time.

Social-aware routing for cognitive radio–based vehicular ad hoc networks

Cognitive radio–based vehicular ad hoc networks can solve the problem of limited spectrum resource and growing vehicular communication service demands in intelligent transportation systems, and thus, it receives much concern recently. In cognitive radio–based vehicular ad hoc networks, the high mobility of vehicles and the dynamic spectrum activity of cognitive radio make routing in such networks a great challenge. Some routing researches have been proposed in cognitive radio–based vehicular ad hoc networks with single-objective optimization and neglecting the nodes’ social behaviors which can improve the network performance. From this perspective, we propose a social-aware routing scheme for cognitive radio–based vehicular ad hoc networks, with the purpose of increasing the packet delivery ratio and decreasing the overhead ratio. First, we analyze the social centrality of primary users to offer an accuracy spectrum hole measurement. Moreover, we develop a social community partition algorithm to divide secondary users into intra-community and inter-community groups. Furthermore, considering the tradeoff between the packet delivery ratio and the overhead ratio, we adopt different replication policies and forwarding ranks in different community communication processes. In the intra-community communication process, we employ the single-copy policy and the contact duration rank. In the inter-community communication process, we utilize the optimized-binary-tree replication policy and the bridge coefficient rank. Simulation results show that our social-aware routing scheme achieves the higher package delivery ratio and the lower overhead ratio when compared with the existing cognitive radio–based vehicular ad hoc networks routing schemes and other standard routing schemes.

Malware homology identification based on a gene perspective

Malware homology identification is important in attacking event tracing, emergency response scheme generation, and event trend prediction. Current malware homology identification methods still rely on manual analysis, which is inefficient and cannot respond quickly to the outbreak of attack events. In response to these problems, we propose a new malware homology identification method from a gene perspective. A malware gene is represented by the subgraph, which can describe the homology of malware families. We extract the key subgraph from the function dependency graph as the malware gene by selecting the key application programming interface (API) and using the community partition algorithm. Then, we encode the gene and design a frequent subgraph mining algorithm to find the common genes between malware families. Finally, we use the family genes to guide the identification of malware based on homology. We evaluate our method with a public dataset, and the experiment results show that the accuracy of malware classification reaches 97% with high efficiency.

Assessment of 3DTV-related fatigue with resting-state fMRI

Objective:This paper studies the fatigue caused by prolonged watching of three-dimensional television (3DTV) by applying the amplitude of low frequency fluctuation (ALFF) and the community partitioning algorithm in brain functional network techniques to resting-state functional magnetic resonance imaging (fMRI). In this study, two specific frequency bands (Slow5: 0.01–0.027 Hz and Slow4: 0.027–0.073 Hz) were analyzed to investigate the change of brain spontaneous activity after one-hour watching of 3DTV. Meanwhile, the changes of brain modular organization were analyzed to discuss the physiological mechanism of brain fatigue during long period of watching 3DTV. Methods:Valid data of resting state fMRI from 20 dextromanual subjects were adopted before and after 1 h watching of 3DTV/2DTV. Then, ALFF was processed using SPM8 and REST software to analyze the variation of resting-state fMRI induced by watching 2D/3DTV. Community partitioning algorithm was used to evaluate the change of brain modular organization. Results:While watching 2DTV does not significantly change the brain spontaneous activity, watching 3DTV makes significant differences on middle frontal gyrus (BA9/BA10), right inferior occipital gyrus (BA18/19), right inferior temporal gyrus (BA20/37) and cingulate cortex (BA32). The result shows that dorsal stream was activated after watching 3DTV through analyzed modularity and community, which verifies that watching 3DTV requires the brain visual pathway to process stereoscopic display. Meanwhile, the result further reflects that the neural circuit, including visual conducting pathway, temporal lobe and basal ganglia loop, is closely related to brain fatigue caused by long period of watching 3DTV.The study has shown the potential risk of 3DTV to viewers’ health, especially on vision and recognition functions. The basal ganglia neurons play crucial roles in the physiological mechanism of brain fatigue. Therefore, prolonged 3DTV watching should be avoided particularly for physically developing teenagers. This research suggested that the ALFF technique and brain modular organization is able to reveal intrinsic changes caused by visual fatigue and provided a foundation for 3DTV fatigue assessment.

An overlapping network community partition algorithm based on semi-supervised matrix factorization and random walk

Abstract The discovery of community structure is the basis of understanding the topology structure and social function of the network. It is also an important factor for recommendation technology, information dissemination, event prediction, and more. In this paper, we consider the structure and characteristics of the social network and propose an algorithm based on semi-supervised matrix factorization and random walk. The proposed method first calculates the transition probability between nodes through the topology of the network. The random walk model is then used to obtain the final walk probability, and the feature matrix is constructed. At the same time, we combine a priori content information in the network to build a must-link matrix and a cannot-link matrix. We then merge them into the feature matrix of the random walk to form a new feature matrix. Finally, the expectation of the number of edges is defined according to the factorized membership matrix. Results demonstrate the effectiveness and better performance of our method.

An Improved Community Partition Algorithm Integrating Mutual Information

The research of community detection can help us analyze kinds of problems in social network, in which the research of community structure is very important. This paper proposes an improved algorithm: An Improved BGLL Integrating Mutual Information (BGLLi), which stabilizes modularity, meanwhile fuses the index of mutual information, so that we can find the optimal threshold of modularity and reduce the running time of community partition effectively. The dataset adopts the Twitter dataset and the public Arxiv dataset, and the related experimental results have verified the effectiveness of the algorithm

An Effective Algorithm for Mining Forum Users Association Rules

Traditional algorithm for mining association rules need to scan the database many times when mining association rules, which are inefficient and time-wasting. In the light of the defects of traditional algorithm, this paper introduces the improvement of community partition algorithm into the process of association mining and uses the Internet forum users’ data of a university as the object of the new algorithm for mining association rules. The experiment shows that the new algorithm can help optimize the data association rules mining and reduces the times and time of scanning the database, so the mining efficiency is greatly enhanced.

SMART: A Social- and Mobile-Aware Routing Strategy for Disruption-Tolerant Networks

Disruption-tolerant networks (DTNs) are sparse mobile ad hoc networks where nodes connect with each other intermittently and end-to-end communication paths do not exist. Data routing in DTNs is challenging and has drawn much attention from research communities recently. Although many DTN routing strategies have been proposed in the past, they confront problems such as blind spots and dead ends and lack efficient implementation in a decentralized, large-scale, mobile, and dynamic environment. To overcome these difficulties, we introduce a new solution for DTNs that leverages the social properties and mobility characteristics of users. Our observation of the mobile trajectories of three data sets collected from real DTNs reveals that user movements appear locally and they tend to form communities correlated to geographic locations. Based on these findings, we propose a social- and mobile-aware routing strategy (SMART). It exploits a distributed community partitioning algorithm to divide the DTN into smaller communities regarding user locations and interaction routines. For intracommunity communications, a decayed routing metric convoluting social similarity and social centrality is calculated, which is used to decide forwarding node efficiently while avoiding the blind-spot and dead-end problems. To enable efficient intercommunity communications, we choose the fringe nodes that travel remotely as relays, and we propose the node-to-community utilities for routing decision across communities. We present empirical analysis to show that SMART reduces the occurrence of blind spots and dead ends to a level below 1%. The efficiency of SMART is evaluated by extensive trace-driven experiments, which illustrate that it outperforms other routing strategies in various real DTN traces.

A Collaborative Recommend Algorithm Based on Bipartite Community

The recommendation algorithm based on bipartite network is superior to traditional methods on accuracy and diversity, which proves that considering the network topology of recommendation systems could help us to improve recommendation results. However, existing algorithms mainly focus on the overall topology structure and those local characteristics could also play an important role in collaborative recommend processing. Therefore, on account of data characteristics and application requirements of collaborative recommend systems, we proposed a link community partitioning algorithm based on the label propagation and a collaborative recommendation algorithm based on the bipartite community. Then we designed numerical experiments to verify the algorithm validity under benchmark and real database.