A maximal clique based multiobjective evolutionary algorithm for overlapping community detection

Detecting community structure has become one im-portant technique for studying complex networks. Although many community detection algorithms have been proposed, most of them focus on separated communities, where each node can be-long to only one community. However, in many real-world net-works, communities are often overlapped with each other. De-veloping overlapping community detection algorithms thus be-comes necessary. Along this avenue, this paper proposes a maxi-mal clique based multiobjective evolutionary algorithm for over-lapping community detection. In this algorithm, a new represen-tation scheme based on the introduced maximal-clique graph is presented. Since the maximal-clique graph is defined by using a set of maximal cliques of original graph as nodes and two maximal cliques are allowed to share the same nodes of the original graph, overlap is an intrinsic property of the maximal-clique graph. Attributing to this property, the new representation scheme al-lows multiobjective evolutionary algorithms to handle the over-lapping community detection problem in a way similar to that of the separated community detection, such that the optimization problems are simplified. As a result, the proposed algorithm could detect overlapping community structure with higher partition accuracy and lower computational cost when compared with the existing ones. The experiments on both synthetic and real-world networks validate the effectiveness and efficiency of the proposed algorithm

Review of Community Detection in Complex Brain Networks

The brain network community detection algorithm has become a highly regarded topic in recent years within the fields of neuroscience and network science, widely employed to unveil patterns of structural and functional connectivity in the brain. Due to the complexity of the brain networks and the need to handle multiple subjects and various task scenarios, it significantly increases the difficulty of community detection in this field. This paper focuses on functional magnetic resonance imaging (fMRI) technology and comprehensively reviews the advancements in research regarding algorithms for detecting communities within brain functional networks. Firstly, the basic process, task categories, and method types of brain network community detection algorithms are described. Next, various brain network community detection algorithms are classified in different task scenarios, including separate communities, overlapping communities, hierarchical communities, and dynamic community detection algorithms. A detailed analysis of the advantages and disadvantages of different methods is provided, along with their applicable scopes. Finally, the future directions of brain network community detection algorithms are discussed, including the problem of community detection in multi-subject networks, robustness issues in brain network community detection, and studies on brain network community detection algorithms for multimodal imaging data. This paper can serve as a methodological guide for future research on brain network community structures

Abnormal baseline brain activity in non-depressed Parkinson's disease and depressed Parkinson's disease: a resting-state functional magnetic resonance imaging study.

Depression is the most common psychiatric disorder observed in Parkinson's disease (PD) patients, however the neural contribution to the high rate of depression in the PD group is still unclear. In this study, we used resting-state functional magnetic resonance imaging (fMRI) to investigate the underlying neural mechanisms of depression in PD patients. Twenty-one healthy individuals and thirty-three patients with idiopathic PD, seventeen of whom were diagnosed with major depressive disorder, were recruited. An analysis of amplitude of low-frequency fluctuations (ALFF) was performed on the whole brain of all subjects. Our results showed that depressed PD patients had significantly decreased ALFF in the dorsolateral prefrontal cortex (DLPFC), the ventromedial prefrontal cortex (vMPFC) and the rostral anterior cingulated cortex (rACC) compared with non-depressed PD patients. A significant positive correlation was found between Hamilton Depression Rating Scale (HDRS) and ALFF in the DLPFC. The findings of changed ALFF in these brain regions implied depression in PD patients may be associated with abnormal activities of prefrontal-limbic network

Spatial-temporal data-driven service recommendation with privacy-preservation

© 2019 Elsevier Inc. The ever-increasing popularity of web service sharing communities have produced a considerable amount of web services that share similar functionalities but vary in Quality of Services (QoS) performances. To alleviate the heavy service selection burden on users, lightweight recommendation ideas, e.g., Collaborative Filtering (CF) have been developed to aid users to select their preferred services. However, existing CF methods often face two challenges. First, service QoS is often context-aware and hence depends on the spatial and temporal information of service invocations heavily. While it requires challenging efforts to integrate both spatial and temporal information into service recommendation decision-making process simultaneously. Second, the location-aware and time-aware QoS data often contain partial sensitive information of users, which raise an emergent privacy-preservation requirement when performing service recommendations. In view of above two challenges, in this paper, we integrate the spatial-temporal information of QoS data and Locality-Sensitive Hashing (LSH) into recommendation domain and bring forth a location-aware and time-aware recommendation approach considering privacy concerns. At last, a set of experiments conducted on well-known WS-DREAM dataset show the feasibility of our approach

Alzheimer's disease-related changes in regional spontaneous brain activity levels and inter-region interactions in the default mode network

The degree of Granger causal modeling estimated influence for a brain region was reported to predict its blood oxygenation level-dependent (BOLD) activity level in the resting-state default mode network (DMN). Many brain disorders, such as Alzheimer's disease (AD), may alter the influence strength, activity levels, or both. Whether the relationship or prediction between these two will be affected under disease condition is unknown. In this study, the spontaneous brain activity, and inter-regional Granger causality connection were investigated over eight core DMN regions in AD patients in contrast to that in normal controls. Compared to normal control (NC), AD patients had both decreased BOLD activity level and Granger causal influence in medial prefrontal cortex and decreased activity level in inferior parietal cortex showed. However, the positive correlation between the BOLD activity level and the degree of the Granger causal modeling defined influence was found not altered by AD. (C) 2013 Elsevier B.V. All rights reserved

Statistical parametric map showing the significant differences in the ALFF between three groups: depressed PD patients, non-depressed PD patients and NCs.

<p>A) The differences between depressed PD patients and non-depressed PD patients. B) The differences between non-depressed PD patients and NCs. C) The differences between depressed PD patients and NCs. The threshold for display was set to p<0.005, cluster size> = 432 mm³.</p

Clinical and demographic characteristics.

<p>Note: Abbreviations: HY–Hoehn and Yahr; UPDRS–Unified Parkinson’s Disease Rating Scale; MMSE–Mini Mental state examination.</p

Brain regions exhibiting an altered ALFF between depressed PD patients and non-depressed PD patients.

<p>BA: Brodmann area; MNI: Montreal neurological institute.</p