Performance Of Community Detection Research Articles

Integrating topology and content equally in non-negative matrix factorization for community detection

Non-negative matrix factorization (NMF) is widely used in analyzing the community structure of complex networks. However, most of the current related efforts only focus on the network structural topology, while neglecting the node content. Although there have been efforts to integrate topology and content, inefficient strategies balancing their contributions can also lead to unsatisfactory results. In this work, we treat topology and content equally and propose two novel NMF-based methods to finely balance topology and content to improve the performance of community detection. We first propose a double encoder–decoder-based NMF (DEDNMF) method that employs two NMF-based encoder–decoders to integrate topology and content adaptively. Experimental results show that DEDNMF outperforms the baseline methods with average improvements of 50.69% and 46.93% in terms of normalized mutual information (NMI) and adjusted rand index (ARI) on all experimental networks, respectively. Moreover, we propose a double-decoder-based NMF (DDNMF) method that uses only two decoders for both topology and content, imposing sparsity constraints on NMF. Experimental results show that DDNMF outperforms the baseline methods with average improvements of 72.11% and 77.00% in terms of NMI and ARI on all experimental networks. Experimental results also show that the proposed two methods have good convergence and robustness.

Cascaded Semantic Fractionation for identifying a domain in social media.

Searching social media to find relevant semantic domains often results in large text files, many of which are irrelevant due to cross-domain content resulting from word polysemy, abstractness, and degree centrality. Through an iterative pruning process, Cascaded Semantic Fractionation (CSF) systematically removes these cross-domain links. The social network procedure performs community detection in semantic networks, locates the semantic groups containing the terms of interest, excludes intergroup links, and repeats community detection on the pruned intragroup network until the domain of interest is clarified. To illustrate CSF, we analyzed public Facebook posts, using the CrowdTangle app for historical data search, from February 3, 2020, to March 13, 2021, about the possible Wuhan lab leak of COVID-19 over a daily interval. The initial search using keywords located six multi-day bursts of posts of more than 500 per day among 95 K posts. These posts were network analyzed to find the domain of interest using the iterative community detection and pruning process. CSF can be applied to capture the evolutions in semantic domains over time. At the outset, the lab leak theory was presented in conspiracy theory terms. Over time, the conspiratorial elements washed out in favor of an accidental release as the issue moved from social to mainstream media and official government views. CSF identified the relevant social media semantic domain and tracked its changes.

Changes in Community Structure of Brain Dynamic Functional Connectivity States in Mild Cognitive Impairment

Recent researches have noted many changes of short-term dynamic modalities in mild cognitive impairment (MCI) patients' brain functional networks. In this study, the dynamic functional brain networks of 82 MCI patients and 85 individuals in the normal control (NC) group were constructed using the sliding window method and Pearson correlation. The window size was determined using single-scale time-dependent (SSTD) method. Subsequently, k-means was applied to cluster all window samples, identifying three dynamic functional connectivity (DFC) states. Collective sparse symmetric non-negative matrix factorization (cssNMF) was then used to perform community detection on these states and quantify differences in brain regions. Finally, metrics such as within-community connectivity strength, community strength, and node diversity were calculated for further analysis. The results indicated high similarity between the two groups in state 2, with no significant differences in optimal community quantity and functional segregation (p < 0.05). However, for state 1 and state 3, the optimal community quantity was smaller in MCI patients compared to the NC group. In state 1, MCI patients had lower within-community connectivity strength and overall strength than the NC group, whereas state 3 showed results opposite to state 1. Brain regions with statistical difference included MFG.L, ORBinf.R, STG.R, IFGtriang.L, CUN.L, CUN.R, LING.R, SOG.L, and PCUN.R. This study on DFC states explores changes in the brain functional networks of patients with MCI from the perspective of alterations in the community structures of DFC states. The findings could provide new insights into the pathological changes in the brains of MCI patients.

Interaction behavior enhanced community detection in online social networks

Community detection in online social networks (OSNs) has attracted a great deal of attention in recent years due to its potential prospects in applications such as recommendation systems, spam detection, targeted advertising, etc. However, community detection methods that have been proposed so far rely mostly on static information, such as the topology of the network, the attributes of the users, etc. In this paper, we propose a framework that enhances community detection with user interaction behaviors in OSNs since such behaviors should be important factors in characterizing the communities. In the framework, the various features of user interaction behaviors are expressed in the form of interaction indices through utilizing the cumulative distribution function and interaction divergence. The indices are then transformed into edge weights through a logistic regression model and combined with the static information to construct a weighted graph for the OSN. Finally, community detection is realized by applying a modularity maximization algorithm. A comprehensive evaluation using diverse real-world datasets shows that our proposed framework has superior performance over comparable ones in deriving high-quality communities. Critical metrics, such as modularity, average clustering coefficient, and average conductance, are evaluated to demonstrate the effectiveness of our approach. Furthermore, the synergy between network topology and user interaction behavior in community detection is also investigated.

Detecting communities in attributed networks through bi-direction penalized clustering and its application

Exploiting heterogeneous information in attributed networks to improve the performance of community detection has attracted considerable research attention. Although variational graph autoencoder (VGAE)-based methods have been proven to be effective strategies, they perform community detection based on assumptions regarding the dimension of embedding and the number of communities, limiting their effectiveness and applicability. In this study, we combined VGAE-based methods and a bi-direction penalized clustering algorithm (BiPClust) for community detection. Our approach addresses the issues of dimension selection and community number determination by automatically optimizing penalized clustering. Both the computational algorithm and statistical theorems confirm that BiPClust effectively mitigates the impacts of redundant embedding and determines the unknown number of communities. Furthermore, applying the proposed methods to community detection on benchmark datasets and syndicated investment networks in China reveals that BiPClust surpasses other methods in performance.

Attention Deficit Hyperactivity Disorder (ADHD) and the gut microbiome: An ecological perspective.

Attention Deficit Hyperactivity Disorder (ADHD) is an increasingly prevalent neuropsychiatric disorder characterized by hyperactivity, inattention, and impulsivity. Symptoms emerge from underlying deficiencies in neurocircuitry, and recent research has suggested a role played by the gut microbiome. The gut microbiome is an ecosystem of interdependent taxa involved in an exponentially complex web of interactions, plus host gene and reaction pathways, some of which involve neurotransmitters with roles in ADHD neurocircuitry. Studies have analyzed the ADHD gut microbiome using macroscale metrics such as diversity and differential abundance, and have proposed several taxa as elevated or reduced in ADHD compared to Control. Few studies have delved into the complex underlying dynamics ultimately responsible for the emergence of such metrics, leaving a largely incomplete, sometimes contradictory, and ultimately inconclusive picture. We aim to help complete this picture by venturing beyond taxa abundances and into taxa relationships (i.e. cooperation and competition), using a publicly available gut microbiome dataset (targeted 16S, v3-4 region, qPCR) from an observational, case-control study of 30 Control (15 female, 15 male) and 28 ADHD (15 female, 13 male) undergraduate students. We first perform the same macroscale analyses prevalent in ADHD gut microbiome literature (diversity, differential abundance, and composition) to observe the degree of correspondence, or any new trends. We then estimate two-way ecological relationships by producing Control and ADHD Microbial Co-occurrence Networks (MCNs), using SparCC correlations (p ≤ 0.01). We perform community detection to find clusters of taxa estimated to mutually cooperate along with their centroids, and centrality calculations to estimate taxa most vital to overall gut ecology. We finally summarize our results, providing conjectures on how they can guide future experiments, some methods for improving our experiments, and general implications for the field.

A study on community detection in multilayer networks

Studying complex networks is essential for a better understanding of network science. Many studies have been done on single-layer networks in complex networks. After the advancement and widespread usage of the internet and social media networks, performing community detection in multilayer networks becomes essential to reach more people and work with different personalities on different platforms. Motivated by this observation, this paper has studied types of networks, metrics, measures, and community detection using deep learning-based models in multilayer networks. This survey can play a significant role in analyzing and understanding multilayer networks.

A graph-enhanced attention model for community detection in multiplex networks

Community detection is to divide a graph or a network into several components with high intra-edge density in a single-layer network. However, a multiplex network, consisting of multiple layers which share the same nodes but each owning different types of edges, brings new challenges for community detection. Although many classic algorithms and several multiplex embedding-based models try to address this problem, i.e. community detection in multiplex networks, they mostly focus on the local topological structures but ignoring the global information in each network layer, which is important for encoding intra-layer structural attributes and merging inter-layer semantic relations. In this paper, we propose a graph-enhanced attention model (GEAM) for community detection in multiplex networks by utilizing the above global information. In particular, the GEAM first constructs a layer contrastive learning module to encode node and graph embedding from the local and global graph view in each network layer. We also propose a self-attention adaptive fusion mechanism to learn a comprehensive version of node representation by fusing multiple layers. Finally, we propose an edge density-driven community detection module to train our GEAM in an end-to-end manner and output community divisions with strong modular structures. Experiments on both synthetic and real-world datasets validate the superiority of our GEAM over the state-of-the-art algorithms in terms of better community detection performance.

Unified robust network embedding framework for community detection via extreme adversarial attacks

Graph data are widely available in complex network systems. Numerous community detection algorithms have been investigated to study graph problems wherein the network topology offers abundant behavioral and functional information. In real scenarios, networks are extremely susceptible to external perturbations, primarily because of sparse topological information. Consequently, maintaining robust community detection performance when confronted with intricate network attacks or perturbations is challenging. Previous anti-graph perturbation methods have typically been adjusted to specific perturbation types, leading to the degradation or failure of these defences when confronted with other types of attack. Accordingly, a unified robust framework that leverages extreme adversarial attacks is proposed in this paper. Specifically, a novel graph perturbation module is introduced into the model to generate extreme undirected or directed perturbations through dynamic updating; this ensures that the model can fit the attacked network well. The proposed framework derives a unified perturbation formulation capable of simultaneously attacking symmetric and asymmetric networks. Furthermore, this framework can be applied to many existing non-negative matrix factorization-based community detection methods. Extensive experiments on artificial and real-world networks demonstrate that the proposed framework significantly improves the robustness of detection tasks, particularly in noisy networks.

Community Detection by Node Betweenness Using Optimized Girvan-Newman Cuckoo Search Algorithm

Due to technological development, social media platforms like forums and microblogs allow people to share their experiences, thoughts, and feelings. The organization, shopping groups etc. has major discussions regarding their business advertisements and product reviews. Also, there are certain followers for particular person or group due to their interests. Here the major issue is to know who or which group in social media is more influenced. The social media analysis needs to perform for identifying influenced person in the social media. The influencer node/person detection in a certain community is already done using greedy algorithm, genetic algorithm, ant colony optimization, cuckoo search algorithms. These existing techniques takes more time for diffusion and accuracy in prediction is not satisfied by users. To overcome this issues, in this research influencer node is identified using optimized Girvan Newman Cuckoo Search Algorithm (GNCSA). First Grivan Newman is used to identify the community and perform community detection. Cuckoo search algorithm uses host bird strategy in finding cuckoo eggs in his nest. Based on the centrality measure it decides whether the node is an influencer or not. This paper proposed Influencer detection by forming community first and measures angular centrality using optimized Girvan Newman cuckoo search algorithm. Our proposed work GNCSA gives a better accuracy rate for the data sets of Dolphin 0.89, for Facebook dataset got 0.93, Twitter data set got 0.94 and for YouTube data set 0.92, karate club and football got 0.91. This proposed work increases the intracommunity of the social network and improves its performance accurately by detecting the influencer in the social network.

Network Refinement: Denoising complex networks for better community detection

Network models are widely used in many fields for their powerful ability to provide a vivid representation of relationships between variables. Modeling entity relationships as networks can be affected by many factors, such as experimental noise and missing data, making the network structure unclear, which inherently hampers the effectiveness of network-based downstream analyses, especially community detection. Consequently, it is necessary to denoise networks before analyzing networks. However, the significance of the network pre-processing step for downstream analysis has been neglected in most current studies of community detection. On the other hand, existing researches on network denoising are very limited and lack adaptability studies. Specialized denoising methods for improving community detection accuracy are not yet available. In this study, we highlighted the necessity of using network denoising as a pre-processing step to improve the performance of community detection. We proposed a novel network denoising method, called Network Refinement (NR), which used a global diffusion process defined by random walk on graph to enhance the self-organization properties of complex networks. NR took a noisy network as input and output a denoised network with clearer community structure by adjusting the edge weights. We have proved that NR can be understood as a degree normalized version of the Katz index, which renders paths with higher intermediate node degrees less important because their information is dispersed through more adjacent edges. We have showed through sufficient numerical experiments that NR significantly improves the clarity of the network’s mesoscale structure, and NR can be applied as a pre-processing step to substantially boost the performance of various community detection algorithms on both simulated networks and real-world networks.

RGCLN: Relational Graph Convolutional Ladder-Shaped Networks for Signed Network Clustering

Node embeddings are increasingly used in various analysis tasks of networks due to their excellent dimensional compression and feature representation capabilities. However, most researchers’ priorities have always been link prediction, which leads to signed network clustering being under-explored. Therefore, we propose an asymmetric ladder-shaped architecture called RGCLN based on multi-relational graph convolution that can fuse deep node features to generate node representations with great representational power. RGCLN adopts a deep framework to capture and convey information instead of using the common method in signed networks—balance theory. In addition, RGCLN adds a size constraint to the loss function to prevent image-like overfitting during the unsupervised learning process. Based on the node features learned by this end-to-end trained model, RGCLN performs community detection in a large number of real-world networks and generative networks, and the results indicate that our model has an advantage over state-of-the-art network embedding algorithms.

Recovery of Brain Network Integration and Segregation During the Loss and Recovery of Consciousness Induced by Sevoflurane.

Anesthetic-induced loss of consciousness (LOC) has been studied using functional connectivity (FC) and functional network analysis (FNA), manifested as fragmentation of the whole-brain functional network. However, how the fragmented brain networks reversibly recover during the recovery of consciousness (ROC) remains vague. This study aims to investigate the changes in brain network structure during ROC, to better understand the network fragmentation during anesthesia, thus providing insights into consciousness monitoring. We analyzed EEG data recorded from 15 individuals anesthetized by sevoflurane. By investigating the properties of functional networks generated using different brain atlases and performing community detection for functional networks, we explored the changes in brain network structure to understand how fragmented brain networks recover during the ROC. We observed an overall larger FC magnitude during LOC than in the conscious state. The ROC was accompanied by the increasing binary network efficiency, decreasing FC magnitude, and decreasing community similarity with the functional atlas. Furthermore, we observed a negative correlation between modularity and community number ( [Formula: see text] and , linear regression test), in which modularity increased and community number decreased during ROC. Our results show that a larger FC magnitude reveals excessive synchronization of neuronal activities during LOC. The increasing binary network efficiency, decreasing community number, and decreasing community similarity indicate the recovery of functional network integration. The increasing modularity implies the recovery of functional network segregation during ROC. The results suggest the limitation of FC magnitude and modularity in monitoring anesthetized states and the potential of integrated information theory to evaluate consciousness.

Multi-Constrained Embedding for Accurate Community Detection on Undirected Networks

A Symmetric Non-negative Matrix Factorization (SNMF)-based network embedding model adopts a unique Latent Factor (LF) matrix for describing the symmetry of an undirected network, which reduces its representation ability to the target network and thus resulting in accuracy loss when performing community detection. To address this issue, this paper proposes a new undirected network embedding model, i.e., Alternating Direction Method of Multipliers ( <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</u> DMM)-based, <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</u> odularity, <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</u> ymmetry and <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</u> onnegativity-constrained <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</u> mbedding (AMSNE), which can be applicable to undirected, weighted or unweighted networks. It relies on two-fold ideas: a) Introducing the symmetry constraints into the model to correctly describe the symmetric of an undirected network without accuracy loss; and b) Adopting the ADMM principle to efficiently solve its constrained objective. Extensive experiments on eight real-world networks strongly evidence that the proposed AMSNE outperform several state-of-the-art models, making it suitable for real applications.

Community Detection in Semantic Networks: A Multi-View Approach.

The semantic social network is a complex system composed of nodes, links, and documents. Traditional semantic social network community detection algorithms only analyze network data from a single view, and there is no effective representation of semantic features at diverse levels of granularity. This paper proposes a multi-view integration method for community detection in semantic social network. We develop a data feature matrix based on node similarity and extract semantic features from the views of word frequency, keyword, and topic, respectively. To maximize the mutual information of each view, we use the robustness of L21-norm and F-norm to construct an adaptive loss function. On this foundation, we construct an optimization expression to generate the unified graph matrix and output the community structure with multiple views. Experiments on real social networks and benchmark datasets reveal that in semantic information analysis, multi-view is considerably better than single-view, and the performance of multi-view community detection outperforms traditional methods and multi-view clustering algorithms.

The trade-off between topology and content in community detection: An adaptive encoder–decoder-based NMF approach

Community detection is an important research field of complex network analysis and focuses on the study of networks’ aggregation behaviours. Different from traditional methods that only consider network structural topology, many efforts have been put into combining network structural topology with node content attributes to achieve better community detection performance. However, it is critical to make an appropriate trade-off between structural topology and node content. In this paper, we propose an adaptive trade-off approach, called ANMF, which not only considers both structural topology and node content, but also provides a flexible parameter to balance their contribution. Compared with other related approaches, ANMF is a kind of non-negative matrix factorization (NMF)-based community detection method, but it imposes more constraints on the network reconstruction. More precisely, ANMF simultaneously employs a decoder that reconstructs a network from its community membership space and an encoder that transforms the network into the community membership space. Moreover, compared with the most related state-of-the-art effort adaptive semantic community detection (ASCD), which considers the topology part always has more contribution if there is a mismatch, ANMF considers the mismatch in two different situations, i.e., the topology part contributes more than the node content part and the node content part contributes more than the topology part. Based on the intensive evaluation on both real and artificial networks, ANMF provides higher normalized mutual information (NMI) values of 4.95%∼126.41% than the models without considering node content information on 13 out of 14 experimental networks. ANMF also presents higher NMI values of 7.38%∼201.01% than ASCD on 13 out of 14 experimental networks. Moreover, ANMF shows good convergence performance, and it can converge after 100 iterations on all of the networks. ANMF also provides stability alike to similar methods in terms of the average NMI standard deviation, which is 0.03 on all of the networks.

Multi-Purpose function-based Parliamentary Optimization Framework for Community Detection over social media

This paper presents a multi-purpose function-based Parliamentary Optimization, community detection methods. Initially, the population of Parliamentary Optimization Algorithm was created in a Python environment for the data used. The population formed was divided into a certain number of groups, and the power values of each group were calculated. While strong groups show joining according to the determined combination probability value, the vulnerable groups are eliminated from the population according to the determined deletion probability. The result for the problem has been approached. The program steps continued until all groups were combined and the termination condition of the algorithm was met; the individual with the highest eligibility values among the remaining data in the last stage was accepted as the solution to the overlapping community discovery problem of the proposed algorithm. Subsequently, performs a proposed work evaluated over one artificial and four real network-based social media data sets. The comparison of feature evaluation is carried out to identify the influence of Single and Multi-Purpose functions on community detection performance. Finally, this paper gives a comparative analysis of proposed MPPOA algorithm worth three heuristic overlap community detection algorithm over six real social media data set.

Community detection and reciprocity in networks by jointly modelling pairs of edges

Abstract To unravel the driving patterns of networks, the most popular models rely on community detection algorithms. However, these approaches are generally unable to reproduce the structural features of the network. Therefore, attempts are always made to develop models that incorporate these network properties beside the community structure. In this article, we present a probabilistic generative model and an efficient algorithm to both perform community detection and capture reciprocity in networks. Our approach jointly models pairs of edges with exact two-edge joint distributions. In addition, it provides closed-form analytical expressions for both marginal and conditional distributions. We validate our model on synthetic data in recovering communities, edge prediction tasks and generating synthetic networks that replicate the reciprocity values observed in real networks. We also highlight these findings on two real datasets that are relevant for social scientists and behavioural ecologists. Our method overcomes the limitations of both standard algorithms and recent models that incorporate reciprocity through a pseudo-likelihood approximation. The inference of the model parameters is implemented by the efficient and scalable expectation–maximization algorithm, as it exploits the sparsity of the dataset. We provide an open-source implementation of the code online.

Inferring functional communities from partially observed biological networks exploiting geometric topology and side information

Cellular biological networks represent the molecular interactions that shape function of living cells. Uncovering the organization of a biological network requires efficient and accurate algorithms to determine the components, termed communities, underlying specific processes. Detecting functional communities is challenging because reconstructed biological networks are always incomplete due to technical bias and biological complexity, and the evaluation of putative communities is further complicated by a lack of known ground truth. To address these challenges, we developed a geometric-based detection framework based on Ollivier-Ricci curvature to exploit information about network topology to perform community detection from partially observed biological networks. We further improved this approach by integrating knowledge of gene function, termed side information, into the Ollivier-Ricci curvature algorithm to aid in community detection. This approach identified essential conserved and varied biological communities from partially observed Arabidopsis protein interaction datasets better than the previously used methods. We show that Ollivier-Ricci curvature with side information identified an expanded auxin community to include an important protein stability complex, the Cop9 signalosome, consistent with previous reported links to auxin response and root development. The results show that community detection based on Ollivier-Ricci curvature with side information can uncover novel components and novel communities in biological networks, providing novel insight into the organization and function of complex networks.

Community detection in subspace of attribute

In recent years, attributed networks are increasingly available for us. How to leverage attribute information to gain a better performance of community detection has attracted growing attention. Most established approaches focus on exploiting attribute information based on the observed homophily assumption that nodes with similar attributes are more likely to be connected. Since not all attributes are truly relevant for the formation of communities, such an assumption may not hold in real-world attributed networks. In many cases, only a subset of attributes are the key factors to make nodes connected to form communities and they may differ largely for different communities. In this paper, we propose a new community detection approach in attributed networks, called SOA, by examining the Subspaces Of Attributes. The basic idea is to regard the community detection as an optimization problem, which aims at learning a new similarity matrix to maximize the homophily. By employing an efficient optimization strategy, the high-quality communities as well as their corresponding relevant attributes (i.e., attribute subspaces) are automatically identified. Extensive experimental results on both synthetic and real-world networks have demonstrated the effectiveness of SOA, and shown its superiority over state-of-the-art approaches.