Dynamic consensus community detection and combinatorial multi-armed bandit

Recent years have witnessed the rapid growth of social network services. Real-world social networks are huge and changing over time. Consequently, the problems in this area have become more complex. Community detection is one of the most important problems in social networks. A good community can be defined as a group of nodes that are highly connected to each other and loosely connected to the nodes outside the community. Regarding the fact that social networks are huge in size, having complete information of the whole network is almost impossible. As a result, the problem of local community detection has become more popular in recent years. The problem of community detection in dynamic networks is well-investigated however, the local community detection is not widely addressed by researchers. In this paper, this problem is investigated by employing a number of existing local community detection algorithms in a dynamic structure. Results are reported in two different experiments. Experimental results show that one of the algorithms (algorithm P) outperforms other algorithms regarding the employed dynamic structure. The results indicate that algorithm P is much faster than other local community detection algorithms in dynamic networks.

In the last decade, community detection in dynamic networks has received increasing attention, because it can not only uncover the community structure of the network at any time but also reveal the regularity of dynamic networks evolution. Although methods based on the framework of evolutionary clustering are promising for dynamic community detection, there is still room for further improvement in the snapshot quality and the temporal cost. In this study, a dynamic community detection algorithm based on optional pathway guide pity beetle algorithm (DYN-OPGPBA), which is a novel dynamic community detection method based on the framework of evolutionary clustering, is proposed. We propose an improved PBA for community detection of the network at the first time step, including a discrete search strategy based on adjacent nodes, a closeness-based community modification strategy and a crowded community split strategy. Compared with many representative static community detection methods, the proposed method has some superior detection accuracy. A neighbour vector competition-based individual update strategy and an external population size restriction mechanism are also proposed for community detection at subsequent time steps. Results show that DYN-OPGPBA has a better balance between snapshot quality and temporal cost than two representative dynamic community detection methods.

Most real-world social networks are inherently dynamic, composed of communities that are constantly changing in membership. To track these evolving communities, we need dynamic community detection techniques. This article evaluates the performance of a set of game-theoretic approaches for identifying communities in dynamic networks. Our method, D-GT (Dynamic Game-Theoretic community detection), models each network node as a rational agent who periodically plays a community membership game with its neighbors. During game play, nodes seek to maximize their local utility by joining or leaving the communities of network neighbors. The community structure emerges after the game reaches a Nash equilibrium. Compared to the benchmark community detection methods, D-GT more accurately predicts the number of communities and finds community assignments with a higher normalized mutual information, while retaining a good modularity.

Identifying communities in dynamic networks is essential for exploring the latent network structures, understanding network functions, predicting network evolution, and discovering abnormal network events. Many dynamic community detection methods have been proposed from different viewpoints. However, identifying the community structure in dynamic networks is very challenging due to the difficulty of parameter tuning, high time complexity and detection accuracy decreasing as time slices increase. In this paper, we present a dynamic community detection framework based on information dynamics and develop a dynamic community detection algorithm called DCDID (dynamic community detection based on information dynamics), which uses a batch processing technique to incrementally uncover communities in dynamic networks. DCDID employs the information dynamics model to simulate the exchange of information among nodes and aims to improve the efficiency of community detection by filtering out the unchanged subgraph. To illustrate the effectiveness of DCDID, we extensively test it on synthetic and real-world dynamic networks, and the results demonstrate that the DCDID algorithm is superior to the representative methods in relation to the quality of dynamic community detection.

The problem of community detection has a long tradition in the area of data mining research and has many challenging aspects, particularly with regard to community detection in a time-varying context. Temporal smoothness is an important concept in this domain treated specifically by Evolutionary clustering. However, this approach is mostly designed for networks with low changes during different time steps. In this research, we present a new dynamic community detection algorithm in a representative-based category called ARTISON, which is inspired by the Adaptive Resonance Theory technique- a famous adaptive clustering model in neural networks. The proposed model is able to capture both low and high changes in the networks in binary. Further, our approach recognizes the number of communities automatically in both weighted and binary networks. Detailed experiments utilizing the MATLAB platform have yielded encouraging results for different measures and further motivate the application of such bio-inspired models in a dynamic social networking context.

Dynamic community detection, which is capable of revealing changes in community structure over time, has garnered increasing attention in research. While evolutionary clustering methods have proven to be effective in tackling this issue, they often have a tendency to favor what are referred to as elite solutions, inadvertently neglecting the potential value of non-elite alternatives. Although elite solutions can ensure population convergence, they may result in negative population migration due to the lack of diversity when the network changes. In contrast, when the network undergoes changes, non-elite solutions could better adapt to the changed network, thereby can help the algorithm find accurate community structures in the new environment. To this end, we propose a diversified population migration strategy that consists of two-stages, i.e., solution selection and solution migration. In the first stage, we use elite solutions not only to ensure convergence but also non-elite solutions to maintain diversity and cope with network changes. In the second stage, the migration solutions are refined by using incremental changes between the two consecutive snapshots of networks. Based on the proposed strategy, we suggest a diversified population migration-based multiobjective evolutionary algorithm named DPMOEA. In DPMOEA, we design new genetic operators that utilize incremental changes between networks to make the population evolve in the right direction. Our experimental results demonstrate that the proposed method outperforms state-of-the-art baseline algorithms and can effectively solve the dynamic community detection problem.

Community structure is found everywhere from simple networks to real world complex networks. The problem of community detection is to predict clusters of nodes that are densely connected among themselves. The task of community detection has a wide variety of applications ranging from recommendation systems, advertising, marketing, epidemic spreading, cancer detection etc. The two mainly existing approaches for community detection, namely, stochastic block model and modularity maximization model focus on building a low dimensional network embedding to reconstruct the original network structure. However the mapping to low dimensional space in these methods is purely linear. Understanding the fact that real world networks contain non-linear structures in abundance, aforementioned methods become less practical for real world networks. Considering the nonlinear representation power of deep neural networks, several solutions based on autoencoders are being proposed in the recent literature. In this work, we propose a deep neural network architecture for community detection wherein we stack multiple autoencoders and apply parameter sharing. This method of training autoencoders has been successfully applied for the problems of link prediction and node classification in the literature. Our enhanced model with modified architecture produced better results compared to many other existing methods. We tested our model on a few benchmark datasets and obtained competitive results.

Community detection has been widely studied in the areas of social network analysis and recommendation system. However, most existing research focus on cases where relationships are explicit or depend on simultaneous appearance. In this paper, we propose to study the community detection problem where the relationships are not based on simultaneous appearance, but time-delayed appearances. In other words, we aim to capture the relationship where one individual physically follows another individual. In our attempt to capture such relationships, the major challenge is the presence of spatial homophily, i.e., individuals are attracted to locations due to their popularities and not because of communications. In tackling the community detection problem with spatial homophily and delayed responses, we make the following key contributions: (1) We introduce a four-phase framework, which by way of using quantified impacts excludes homophily. (2) To validate the framework, we generate a synthetic dataset based on a known community structure and then infer that community structure. (3) Finally, we execute this framework on a real-world dataset with more than 6,000 taxis in Singapore. Our results are also compared to those of a baseline approach without homophily-elimination.

Many famous online social networks, e.g., Facebook and Twitter, have achieved great success in the last several years. Users in these online social networks can establish various connections via both social links and shared attribute information. Discovering groups of users who are strongly connected internally is defined as the community detection problem. Community detection problem is very important for online social networks and has extensive applications in various social services. Meanwhile, besides these popular social networks, a large number of new social networks offering specific services also spring up in recent years. Community detection can be even more important for new networks as high quality community detection results enable new networks to provide better services, which can help attract more users effectively. In this paper, we will study the community detection problem for new networks, which is formally defined as the “New Network Community Detection” problem. New network community detection problem is very challenging to solve for the reason that information in new networks can be too sparse to calculate effective similarity scores among users, which is crucial in community detection. However, we notice that, nowadays, users usually join multiple social networks simultaneously and those who are involved in a new network may have been using other well-developed social networks for a long time. With full considerations of network difference issues, we propose to propagate useful information from other well-established networks to the new network with efficient information propagation models to overcome the shortage of information problem. An effective and efficient method, Cat (Cold stArT community detector), is proposed in this paper to detect communities for new networks using information from multiple heterogeneous social networks simultaneously. Extensive experiments conducted on real-world heterogeneous online social networks demonstrate that Cat can address the new network community detection problem effectively.

Sex steroid hormones have been shown to alter regional brain activity, but the extent to which they modulate connectivity within and between large-scale functional brain networks over time has yet to be characterized. Here, we applied dynamic community detection techniques to data from a highly sampled female with 30 consecutive days of brain imaging and venipuncture measurements to characterize changes in resting-state community structure across the menstrual cycle. Four stable functional communities were identified, consisting of nodes from visual, default mode, frontal control, and somatomotor networks. Limbic, subcortical, and attention networks exhibited higher than expected levels of nodal flexibility, a hallmark of between-network integration and transient functional reorganization. The most striking reorganization occurred in a default mode subnetwork localized to regions of the prefrontal cortex, coincident with peaks in serum levels of estradiol, luteinizing hormone, and follicle stimulating hormone. Nodes from these regions exhibited strong intranetwork increases in functional connectivity, leading to a split in the stable default mode core community and the transient formation of a new functional community. Probing the spatiotemporal basis of human brain–hormone interactions with dynamic community detection suggests that hormonal changes during the menstrual cycle result in temporary, localized patterns of brain network reorganization.

A trending issue in the network system that aids in learning and understanding the overall network structure is the community detection in the social network. Actually, they are the dividing wall which divides the node of the network into several subgroups. While dividing, the nodes within the subgroups will get connected densely but, their connections will be sparser between the subgroups. The ultimate objective of the community detection method is to divide the network into dense regions of the graph. But, in general, those regions will correlate with close related entities which can be then said that it is belonging to a community. It is defined based on the principle that the pair of nodes will be connected only if they belong to the same community and if they don’t share the communities, they are less likely to be connected. The vital problems across various research fields like the detection of minute and scattered communities have been necessitated with the ever growing variety of the social networks. The problem of community detection over the time has been recognized with the literature survey and the proposal methodology of set theorem to find the communities detection where the group belongs to activities. In addition to this, several basic concepts are stated in an exhaustive way where the research fields arise from social networks.

In vehicular edge computing (VEC) systems, vehicles can contribute their computing resources to the network, and help other vehicles or pedestrians to process their computation tasks. However, the high mobility of vehicles leads to a dynamic and uncertain vehicular environment, where the network topologies, channel states and computing workloads vary fast across time. Therefore, it is challenging to design task offloading algorithms to optimize the delay performance of tasks. In this chapter, we consider the task offloading among vehicles, and design learning-based task offloading algorithms based on the multi-armed bandit (MAB) theory, which enable vehicles to learn the delay performance of their surrounding vehicles while offloading tasks. We start from the single offloading case where each task is offloaded to one vehicle to be processed, and propose an adaptive learning-based task offloading (ALTO) algorithm, by jointly considering the variations of surrounding vehicles and the input data size. To further improve the reliability of the computing services, we introduce the task replication technique, where the replicas of each task is offloaded to multiple vehicles and processed by them simultaneously, and propose a learning-based task replication algorithm (LTRA) based on combinatorial MAB. We prove that the proposed ALTO and LTRA algorithms have bounded learning regret, compared with the genie-aided optimal solution. And we also build a system level simulation platform to evaluate the proposed algorithms in the realistic vehicular environment.

Community detection is of great importance for online social network analysis. The volume, variety and velocity of data generated by today's online social networks are advancing the way researchers analyze those networks. For instance, real-world networks, such as Facebook, LinkedIn and Twitter, are inherently growing rapidly and expanding aggressively over time. However, most of the studies so far have been focusing on detecting communities on the static networks. It is computationally expensive to directly employ a well-studied static algorithm repeatedly on the network snapshots of the dynamic networks. We propose DynaMo, a novel modularity-based dynamic community detection algorithm, aiming to detect communities of dynamic networks as effective as repeatedly applying static algorithms but in a more efficient way. DynaMo is an adaptive and incremental algorithm, which is designed for incrementally maximizing the modularity gain while updating the community structure of dynamic networks. In the experimental evaluation, a comprehensive comparison has been made among DynaMo, Louvain (static) and 5 other dynamic algorithms. Extensive experiments have been conducted on 6 real-world networks and 10,000 synthetic networks. Our results show that DynaMo outperforms all the other 5 dynamic algorithms in terms of the effectiveness, and is 2 to 5 times (by average) faster than Louvain algorithm.

Crowdsensing Data Trading (CDT), through which a platform can aggregate some data collected by a group of mobile users with sensing devices (a.k.a., data sellers) and sell the corresponding statistics to data consumers, has been recognized as a promising paradigm for large-scale data trading in recent years. It is critical to select sellers with high sensing qualities and maximize all trading participants' profits simultaneously. However, most existing CDT systems either assume that sellers' sensing qualities are known in advance or cannot realize concurrent profit maximization. In this paper, we propose a data trading mechanism based on Combinatorial Multi-Armed Bandit and three-stage Hierarchical Stackelberg game, called CMAB-HS, to tackle the problem of quality unknown seller selection and incentive strategy design. Our objective is to select a group of sellers to maximize the total sensing quality within time budget, and determine the optimal incentive strategy for each participant to maximize individual profit simultaneously. We theoretically prove that CMAB-HS achieves Stackelberg Equilibrium and a tight bound on regret. Additionally, we demonstrate its significant performances through extensive simulations on real data traces.

Information dynamics algorithm for detecting communities in networks