Finding Communities In Networks Research Articles

NDI-PPO: Node degree increment-based proximal policy optimization method for employment community discovery in online social networks

Purpose: aiming at the limitations of pre-input parameters in the complex network overlapping community discovery algorithm based on tag propagation in real networks and the problems of tag redundancy, method: a node degree increment-based proximal policy optimization method for community discovery in online social networks is proposed (named NDI-PPO). Process: by applying the cohesion idea and introducing the concept of modularity increment, a social network great community is constructed from the bottom up according to the criteria of community division. For the problem that the number of iterative steps is sensitive to the strategy gradient algorithm, we adopt an improved PPO to improve the efficiency of feature extraction. In label updating, the maximum clique is used as the core unit to update the labels and weights of the maximum maximum clique adjacent nodes from the center to the periphery using intimacy, and the weights of the non-maximum maximum clique adjacent nodes are updated by means of the maximum weight. In the post-processing stage, the adaptive threshold method is used to remove the noise in the node label, which effectively overcomes the limitation of the number of pre-input overlapping communities in the real network. Result: The simulation results show that the proposed community discovery algorithm NDI-PPO is superior to other advanced algorithms, the time complexity is greatly reduced, and it is suitable for community discovery in large social networks.

Higher Order Knowledge Transfer for Dynamic Community Detection With Great Changes

Network structure evolves with time in the real world, and the discovery of changing communities in dynamic networks is an important research topic that poses challenging tasks. Most existing methods assume that no significant change occurs; namely, the difference between adjacent snapshots is slight. However, great change exists in the real world usually. The great change in the network will result in the community detection algorithms are difficulty obtaining valuable information from the previous snapshot, leading to negative transfer for the next time steps. This paper focuses on dynamic community detection with substantial changes by integrating higher-order knowledge from the previous snapshots to aid the subsequent snapshots. Moreover, to improve search efficiency, a higher-order knowledge transfer strategy is designed to determine first-order and higher-order knowledge by detecting the similarity of the adjacency matrix of snapshots. In this way, our proposal can keep the advantages of previous community detection results and transfer them to the next task. We conduct the experiments on four real-world networks, including the networks with great or minor changes. Experimental results in the low-similarity datasets demonstrate that higher-order knowledge is more valuable than first-order knowledge when the network changes significantly and keeps the advantage even if handling the high-similarity datasets. Our proposal can also guide other dynamic optimization problems with great changes.

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The cover image is based on the Original Article Research on symptoms composition, time series evolution, and network visualisation of interstitial cystitis based on complex network community discovery algorithm by Lei Pang et al., https://doi.org/10.1049/syb2.12083image

Research on symptoms composition, time series evolution, and network visualisation of interstitial cystitis based on complex network community discovery algorithm.

We analyzed the symptoms composition of Interstitial Cystitis (IC), the regularity of the evolution of symptoms before and after treatment, and the visualization of the community network, to provide a reference for clinical diagnosis and treatment of Interstitial Cystitis. Based on the outpatient electronic case data of 552 patients with Interstitial Cystitis, we used a complex network community discovery algorithm, directed weighted complex network, and Sankey map to mine the data of the symptoms composition of Interstitial Cystitis, the evolution of symptoms before and after treatment and the visualization of the community network, to analyze the epidemiological characteristics of interstitial cystitis symptoms in the real world. By the community division of the complex network of interstitial cystitis symptoms, We finally obtained three core symptom communities. Among them, symptom community A (bladder-related symptoms) is the symptom community with the highest proportion of nodes (60.00%) in the complex network of Interstitial Cystitis, symptom community B (non-bladder-related symptoms 1) ranks second (32.00%) in a complex network of Interstitial Cystitis, and symptom community C (non-bladder-related symptoms 2) has the lowest proportion (8.00%). There is a complex evolutionary relationship between the symptoms of Interstitial Cystitis before and after treatment. Among the single symptoms before and after treatment, the decreased rate of Day frequency is 93.22%, and the reduced urgency rate is 93.07%. The decline rate of Nocturia was 82.33%. From the perspective of different communities, theoverall symptoms of symptom community A decreased by 34.39% after treatment, the general symptoms of symptom community B decreased by 35.37%, and the prevalent symptoms of symptom community C decreased by 71.43%. In the case of using diet regulation treatment to treat bladder pain, the cure rate of bladder pain can reach 22.67%. The cure rate of burning in bladders can get 15.38% with Percutaneous Sacral neuromodulation, 96.95% with diet regulation treatment, and 100% with Percutaneous Sacral neuromodulation. When using behavioral physiotherapy to treat bladder pain, 3.57% of the patient's symptoms change to bladder discomfort; 4% of the patient's symptoms change to bladder discomfort when using oral medicine to treat bladder pain.Symptom research methods based on community findings can effectively explore the rule of symptom outcome of Interstitial Cystitis before and after treatment, and the results are highly interpretable by professionals. The cover image is based on the Original Article Research on symptoms composition, time series evolution, and network visualisation of interstitial cystitis based on complex network community discovery algorithm by Lei Pang etal., https://doi.org/10.1049/syb2.12083.

Time series clustering based on normal cloud model and complex network

When data mining research is conducted, it is difficult to obtain precise domain knowledge to set a similarity threshold. Furthermore, noise and missing values are inevitable. Missing values and noise without pre-processing are challenges for many algorithms. A time-series clustering method is proposed based on the normal cloud model and complex networks. Matrix profile similarity measurement, normal cloud model generation and filtering, cloud model expectation curve weighting, degree centrality reweighting, and community discovery in complex networks are the five stages of the proposed clustering algorithm. Local features are considered, and the effects of missing values are reduced when performing similarity measurements. The normal cloud model can be used to set thresholds adaptively. The Louvain algorithm accomplishes the clustering task in complex networks without specifying the clusters. Experiments are conducted on 94 datasets and are compared with 8 clustering methods in the UCR time-series clustering benchmark study. Experimental results indicate that the proposed method can perform well on many datasets.

Improving temporal smoothness and snapshot quality in dynamic network community discovery using NOME algorithm.

The goal of dynamic community discovery is to quickly and accurately mine the network structure for individuals with similar attributes for classification. Correct classification can effectively help us screen out more desired results, and it also reveals the laws of dynamic network changes. We propose a dynamic community discovery algorithm, NOME, based on node occupancy assignment and multi-objective evolutionary clustering. NOME adopts the multi-objective evolutionary algorithm MOEA/D framework based on decomposition, which can simultaneously decompose the two objective functions of modularization and normalized mutual information into multiple single-objective problems. In this algorithm, we use a Physarum-based network model to initialize populations, and each population represents a group of community-divided solutions. The evolution of the population uses the crossover and mutation operations of the genome matrix. To make the population in the evolution process closer to a better community division result, we develop a new strategy for node occupancy assignment and cooperate with mutation operators, aiming at the boundary nodes in the connection between the community and the connection between communities, by calculating the comparison node. The occupancy rate of the community with the neighbor node, the node is assigned to the community with the highest occupancy rate, and the authenticity of the community division is improved. In addition, to select high-quality final solutions from candidate solutions, we use a rationalized selection strategy from the external population size to obtain better time costs through smaller snapshot quality loss. Finally, comparative experiments with other representative dynamic community detection algorithms on synthetic and real datasets show that our proposed method has a better balance between snapshot quality and time cost.

Application of Graph Theory to Find Minimal Paths Between Two Places for the Transportation Problem

Abstract: Graph theory is used for finding communities in networks. Graphs are used as device for modeling and description of real world network systems such are: transport, water, electricity, internet, work operations schemes in the process of production, construction, etc. Although the content of these schemes differ among themselves, but they have also common features and reflect certain items that are in the relation between each other. In this paper, we study on how graph theory can generate transportation problem using shortest path, we designed the solution for practical problem to find a Minimum Spanning Tree(MST) by using Kruskal’s Algorithm and minimal path between two places and graph search Dijkstra’s algorithm.

Intentional controlled islanding based on dynamic community detection for power grid

Power system controlled islanding is an emergency control method used to stop the propagation of disturbances and avoid blackouts. Intentional controlled islanding (ICI) has been proposed as a corrective measure of last resort to split the power system into several sustainable islands. Complex network community detection is widely used in network partitioning and can be used to solve controlled islanding problems. The objective of this study is to apply the dynamic network community detection method to solve ICI problems. Using dynamic network modelling, the division-agglomeration (Di-Ag) algorithm is proposed. Dynamic modelling presets the computation task of the algorithm in the healthy grid phase. The algorithm balances the efficiency of the algorithm, power imbalance, and power flow disruption, without sacrificing other objectives to optimise one objective. Specifically, the grid topology is taken into account when the grid is divided using betweenness as edge weight. The first step of the Di-Ag algorithm uses the dynamic Girvan–Newman algorithm to achieve the goal of power imbalance. Moreover, only the islands' involved lines (nodes) are updated, which improves the efficiency of the algorithm. In the second step of the Di-Ag algorithm, the algorithm merges the islands to reduce power flow disruption. The IEEE 39-bus and 118-bus system are used to compare the performance of the proposed dynamic modelling method and the static network community discovery method in three different cases.

GWBM: an algorithm based on grey wolf optimization and balanced modularity for community discovery in social networks

GWBM: an algorithm based on grey wolf optimization and balanced modularity for community discovery in social networks

Community Cores: Removing Size Bias from Community Detection

Community discovery in social networks has received a significant amount of attention in the social me- dia research community. The techniques developed by the community have become quite adept at identifying the large communities in a network, but often neglect smaller communities. Evaluation techniques also show this bias, as the resolution limit problem in modular- ity indicates. Small communities, however, account for a higher proportion of a social network’s community membership and reveal important information about the members of these communities. In this work, we intro- duce a re-weighting method to improve both the over- all performance of community detection algorithms and performance on small community detection.

A new nature-inspired optimization for community discovery in complex networks

A new nature-inspired optimization for community discovery in complex networks

Community detection in complex networks: From statistical foundations to data science applications

AbstractIdentifying and tracking community structures in complex networks are one of the cornerstones of network studies, spanning multiple disciplines, from statistics to machine learning to social sciences, and involving even a broader range of application areas, from biology to politics to blockchain. This survey paper aims to provide an overview of some most popular approaches in statistical network community detection as well as the newly emerging research directions such as community extraction with higher‐order features and community discovery in multilayer and multiscale networks. Our goal is to offer a unified view at methodological interconnections and the wide spectrum of interdisciplinary data science applications of network community analysis.This article is categorized under: Data: Types and Structure > Graph and Network Data Statistical Learning and Exploratory Methods of the Data Sciences > Clustering and Classification

Intelligent Link Prediction Management Based on Community Discovery and User Behavior Preference in Online Social Networks

Link prediction in online social networks intends to predict users who are yet to establish their network of friends, with the motivation of offering friend recommendation based on the current network structure and the attributes of nodes. However, many existing link prediction methods do not consider important information such as community characteristics, text information, and growth mechanism. In this paper, we propose an intelligent data management mechanism based on relationship strength according to the characteristics of social networks for achieving a reliable prediction in online social networks. Secondly, by considering the network structure attributes and interest preference of users as important factors affecting the link prediction process in online social networks, we propose further improvements in the prediction process by designing a friend recommendation model with a novel incorporation of the relationship information and interest preference characteristics of users into the community detection algorithm. Finally, extensive experiments conducted on a Twitter dataset demonstrate the effectiveness of our proposed models in both dynamic community detection and link prediction.

Novel social network community discovery method combined local distance with node rank optimization function

In view of that most of the current community discovery methods in social network do not consider node self-transfer and node bias, so that it is not possible to extract the graph features effectively, which leads to the ineffective problem by the community discovery, this paper proposes a novel social network community discovery algorithm (Local Distance Laplace, LDL). First, a Laplace matrix decomposition model is constructed based on the principle of matrix decomposition. Secondly, considering the high cost of global social network information acquisition and calculation, a community discovery model based on local distance is proposed. Finally, the optimal community structure is selected by using the NRO (Node Rank Optimization) function. A comprehensive comparative analysis is made on eleven real and synthetic networks. At the same time, validation analysis is conducted on eleven different social networks (Karate, Dolphins, Lemis, Public book, Football, Celegansnertal, Email, Public blogs, Netscience, Power, Hep_th). The experimental simulation results show that: in the real network, the proposed LDL algorithm improved the overall performance by nearly 7% compared with the seven state of art optimal methods (CoVeC, EDBC, JNMF, EADP, LPANNI, LSA, SCFS). The novel algorithm is reasonable and effective and it can also be extended to multi-scale community discovery.

Optimized Algorithm for Skyline Community Discovery in Multi-Valued Networks

The community discovery problem aims to find the close subgraphs from the traditional network. The traditional network with multiple numerical attributes of nodes is called the multi-valued network. The Skyline Community is the largest connected k-core that is not dominated in a multi-valued network, which can be used to solve multi-objective decision-making problems. In this paper, we focus on the skyline community discovery problem. Firstly, we propose a combined index that includes the degree-neighbor (D-N) index and inverted index to manage the multi-valued network. The index can filter the nodes whose degrees do not satisfy ${k}$ . Simultaneously, it can accelerate the judgment of the neighbor relationship between the nodes and improve the query efficiency. Then, we propose the SCDCI (Skyline Community Discovery Algorithm Based on Combined Index Structure) algorithm to compute the skyline community. The algorithm scans the nodes in turn from the inverted indexes, avoiding lots of redundant calculations. The nodes are scanned in descending order by their attributes, therefore, lots of nodes that cannot form a skyline community do not be processed. Finally, the effectiveness of SCDCI algorithm is verified through experiments. Compared with the existing algorithms, the efficiency of our algorithm is improved a lot.

Evolutionary community discovery in dynamic social networks via resistance distance

Traditional social community discovery methods concentrate mainly on static social networks, but the analysis of dynamic networks is a prerequisite for real-time and personalized social services. Through the study of community changes, the community structure in a dynamic network can be tracked over time, which helps in the mining of dynamic network information. In this paper, we propose a method of tracking dynamic community evolution that is based on resistance distance. Specifically, we model the time-varying features of dynamic networks using the convergence of a resistance-based distance. In our model, the heterogeneity of neighboring nodes can be obtained in the local topology of nodes by analyzing the resistance distance between nodes. We design a community discovery algorithm that essentially discovers community structures on dynamic networks by identifying the so-called core node. During the process of community evolution analysis, both the dynamic contribution of ordinary nodes and core nodes in each community are considered. In addition, to avoid the inclusion of spurious communities in the community structure, we define the notion of noise community and account for it in our algorithm. Experimental results show that the method proposed in this paper can yield better accuracy than other existing methods.

Community leader and transition probability based LPA

Many practice problems can be transformed into complex networks, and complex network community discovery has become a hot research topic in various fields. The classic label propagation algorithm (LPA) can give community partition very quickly, but stability of the algorithm is poor due to random label propagation. To solve this problem, community leader principle is built and transition probability is introduced, a label propagation algorithm based on community leader and transition probability (CTLPA) is proposed. CTLPA selects threatened leaders and their communities according to the community leader principle, and uses the transition probability and the degree of the leader to jointly control the order for community merger, so that the threatened leader continuously devours the communities that threaten him, until a preliminary community partition is formed. To further reduce the number of community, in CTLPA, based on the characteristic of the community structure: close relationship within the community and sparse relationship outside the community, the closest communities are merged, until the final community partition is obtained. The CTLPA is compared with other five classic algorithms on LFR artificially generated networks and several real data sets. The experimental results show that CTLPA is robust in community partition, it always gives the same community partition, while the LPA will give different results from multiple independent runs. The number of community partition and the normalized mutual information (NMI) of the CTLPA are the best in most cases.

ANGEL: efficient, and effective, node-centric community discovery in static and dynamic networks

Community discovery is one of the most challenging tasks in social network analysis. During the last decades, several algorithms have been proposed with the aim of identifying communities in complex networks, each one searching for mesoscale topologies having different and peculiar characteristics. Among such vast literature, an interesting family of Community Discovery algorithms, designed for the analysis of social network data, is represented by overlapping, node-centric approaches. In this work, following such line of research, we propose Angel, an algorithm that aims to lower the computational complexity of previous solutions while ensuring the identification of high-quality overlapping partitions. We compare Angel, both on synthetic and real-world datasets, against state of the art community discovery algorithms designed for the same community definition. Our experiments underline the effectiveness and efficiency of the proposed methodology, confirmed by its ability to constantly outperform the identified competitors.

Determining Network Communities Based on Modular Density Optimization

Background:In today’s world, complex systems are conceptually observed in the form of network structure. Communities inherently existing in the networks have a recognizable elucidation in understanding the organization of networks. Community discovery in networks has grabbed the attention of researchers from multi-discipline. Community detection problem has been modeled as an optimization problem. In broad-spectrum, existing community detection algorithms have adopted modularity as the optimizing function. However, the modularity is not able to identify communities of smaller size as compared to the size of the network.Methods:This paper addresses the problem of the resolution limit posed by modularity. Modular density measure succeeds in countering the resolution limit problem. Finding network communities with maximum modular density is an NP-hard problem In this work, the discrete bat algorithm with modular density as the optimization function is recommended.Results:Experiments are conducted on three real-world datasets. For determining the consistency, ten independent runs of the proposed algorithm has been carried out. The experimental results show that our proposed algorithm produces high-quality community structure along with small size communities.Conclusion:The results are compared with traditional and evolutionary community detection algorithms. The final outcome shows the superiority of discrete bat algorithm with modular density as the optimization function with respect to number of communities, maximum modularity, and average modularity.

Multiobjective particle swarm community discovery arithmetic based on representation learning

SummaryWith the continuous increase of the network scale, the structure of the network has also become complicated. The original community discovery algorithm based on small‐scale static networks has been unable to meet our needs. In order to improve the quality of community division, community discovery algorithms based on multiple optimization functions have been proposed. These multiobjective algorithms have continued to increase in time complexity as the optimization functions increase. The time complexity of the multiobjective community discovery algorithm is reduced, and the particle swarm algorithm has higher efficiency and accuracy in solving multiobjective optimization (MOO) problems. Based on the above background, the purpose of this article is to study a multiobjective particle swarm community discovery algorithm based on representation learning. This article uses the network representation learning method for static network community discovery, and designs an improved multiobjective particle swarm‐based community discovery algorithm (MOPSO‐CD). This randomness effectively prevents the algorithm from falling into a local optimum. At the same time, combined with the MOO algorithm, all the Pareto optimal solution sets are retained to adjust the population to correct the lack of accuracy caused by the randomness of the algorithm. In addition, in order to improve the efficiency of the algorithm, this article introduces an efficient Pareto optimal solution set method. Compared with the traditional MOO strategy, the time complexity of the MOO process is O(n2) Reduced to O(nlogn). Through experimental analysis, MOPSO‐CD has higher efficiency and community discovery quality.