Community Detection Scheme Research Articles

Design and analysis of community detection scheme based on machine learning strategies

design and analysis of community detection scheme based on machine learning strategies

Social Community Detection Scheme Based on Social-Aware in Mobile Social Networks

Currently many factors can influence community detection in mobile social networks, where node mobility is a key factor to influence the stability of community structure. In this paper, we propose a social community detection scheme for mobile social networks based on social-ware, including social attribute similarity, node interest similarity and node mobility. Compared with other community detection schemes, our proposed scheme can accurately detect the communities based on community attribute and node mobility. The experiments show the numbers of detected communities and members in the maximal size community generated by our scheme are both smaller than those of the GN and NM schemes. Additionally, since the nodes (users) both have higher mobility in mobile social networks, the efficiency of our proposed scheme relatively becomes higher. The experiments show when the values of mobility in the test data sets increase, the running time of our proposed scheme decreases when the number of edges is fixed. For example, the running time of our proposed scheme is about 17s when the maximum value of mobility is set as 0.5 and the number of edges is about 16000, and further the running time is only about 13s when the maximum value of mobility is set as 1. Therefore, our proposed scheme can more accurately and efficiently make community detection to increase the stability of mobile community structure.

Probabilistic data structure-based community detection and storage scheme in online social networks

With the wide popularity of various social network sites such as — Facebook, Twitter, and Instagram; processing, storage, and analysis of a large volume of data are becoming challenging issues. In general, social networks are assumed to be graphs having nodes for representation of a group of persons in order to explore the relationship between them for Social Network Analysis (SNA). Analysts claim that interconnections in these networks are the reflection of social structure of individual where personalities with common attributes often occupy similar positions. Such similarities are caused by the prospects, opportunities, sensitivities and perceptions created by these similar network positions. Thus, clustering of these individuals is necessary to analyse their common characteristics. However, most of the existing clustering algorithms considered for community detection in SNA have high memory requirements especially in online social networks. So, to mitigate these issues, this paper proposes a novel clustering scheme for community detection for fast access, storage and retrieval of data using Probabilistic Data Structures (PDS). In the proposed scheme, Bloom filter has been used for clustering and Quotient filter has been used for storage and access of cluster nodes. It has been experimentally proved that the proposed scheme provides significant improvement in computational time which is reduced by 64% and 79% respectively in comparison to the linked list and adjacency matrix. Moreover, Quotient filter based storage schema significantly enhances the effectiveness of the proposed scheme over conventional storage methods.

Social community detection and message propagation scheme based on personal willingness in social network

Personal willingness is one of the most important factors influencing the construction of social community and the message propagation in social network. Personal willingness is used to describe the subjective initiative of node (user) to communicate information with outside world. The personal willingness is greater, the corresponding user is more willing to make communication with outside world, then the user is more likely to join the corresponding community. So, personal willingness may reduce the probability of generating large-scale communities so as to improve the accuracy and reliability of community detection and increase the stability of community structure. This paper proposes a social community detection and message propagation scheme based on personal willingness in social network. In the proposed scheme, the social community detection algorithm extracts node attributes and then uses modularity degree, interest degree and personal willingness to sophisticatedly detect social communities; also, the message propagation method is based on the exponential model, which constructs feature vector by edge feature and node feature, willingness vector by personal willingness and community willingness, and related basic relationship by propagation probability and propagation delay. Based on the Weibo, YouTube and Digg data, the experiments show that our proposed scheme can ensure the stability and reliability of social community detection and add the initiative and effectiveness of message propagation among users.

A novel parallel community detection scheme based on label propagation

Community detection is one of the most important ways to reflect the structures and mechanisms of a social network. The overlapping communities are more in line with the reality of the social networks. In society, the phenomenon of some members sharing memberships among different communities reflects as overlapping communities in the networks. Dealing with big data networks, it is a challenging and computationally complex problem to detect overlapping communities. In this paper, we propose highly scalable variants of a community-detection algorithm in a parallel manner called Label Propagation with nodes Confidence (PLPAC). We introduce MapReduce into our scheme to process the big data in a parallel manner and guarantee the efficiency of community detection. We implemented the algorithm on artificial networks as well as real networks to evaluate the accuracy and speedup of the proposed method. Experimental results on datasets from different scenarios illustrate that the improved label propagation method outperforms the state-of-the-art methods in terms of accuracy and time efficiency.

Early prediction of extreme stratospheric polar vortex states based on causal precursors

AbstractVariability in the stratospheric polar vortex (SPV) can influence the tropospheric circulation and thereby winter weather. Early predictions of extreme SPV states are thus important to improve forecasts of winter weather including cold spells. However, dynamical models are usually restricted in lead time because they poorly capture low‐frequency processes. Empirical models often suffer from overfitting problems as the relevant physical processes and time lags are often not well understood. Here we introduce a novel empirical prediction method by uniting a response‐guided community detection scheme with a causal discovery algorithm. This way, we objectively identify causal precursors of the SPV at subseasonal lead times and find them to be in good agreement with known physical drivers. A linear regression prediction model based on the causal precursors can explain most SPV variability (r2 = 0.58), and our scheme correctly predicts 58% (46%) of extremely weak SPV states for lead times of 1–15 (16–30) days with false‐alarm rates of only approximately 5%. Our method can be applied to any variable relevant for (sub)seasonal weather forecasts and could thus help improving long‐lead predictions.

A social cognitive heuristic for adaptive data dissemination in mobile Opportunistic Networks

It is commonly agreed that data (and data-centric services) will be one of the cornerstones of Future Internet systems. In this context, mobile Opportunistic Networks (OppNets) are one of the key paradigms to efficiently support, in a self-organising and decentralised manner, the growth of data generated by localised interactions between users mobile devices, and between them and nearby smart devices such as IoT nodes. In OppNets scenarios, the spontaneous collaboration among mobile devices is exploited to disseminate data towards interested users. However, the limited resources and knowledge available at each node, and the vast amount of data available in the network, make it difficult to devise efficient schemes to accomplish this task. Recent solutions propose to equip each device with data filtering methods derived from human information processing schemes, known as Cognitive Heuristics. They are very effective methods used by human brains to quickly drop useless information and keep only the most relevant information. Although cognitive-based OppNet solutions proved to be efficient (with limited overheads), they can become less effective when facing dynamic scenarios or situations where nodes cannot fully collaborate with each other, as we show in this paper. One of the reasons is that the solutions proposed so far do not take into account the social structure of the environment where the nodes are moving in. In order to be more effective, the selection of information performed by each node should take into consideration not only the relevance of content for the local device, but also for other devices will encounter in the future due to mobility. To this end, in this paper we propose a social-based data dissemination scheme, based on a cognitive heuristic, known as the Social Circle Heuristic. This heuristic is an evaluation method that exploits the structure of the social environment to make inferences about the relevance of discovered information. We show how the Social Circle Heuristic, coupled with a cognitive-based community detection scheme, can be exploited to design an effective data dissemination algorithm for OppNets. We provide a detailed analysis of the performance of the proposed solution via simulation.

Performance-Aware Mobile Community-Based VoD Streaming Over Vehicular Ad Hoc Networks

Leveraging the development of mobile communication technologies and increased capabilities of mobile devices, mobile multimedia services have set new trends. To support high-quality Video-on-Demand (VoD) in mobile wireless networks, using virtual community-based approaches to balance the efficiency of content sharing and maintenance cost of performance-aware solutions has attracted increasing research interest. In this paper, we propose a novel Performance-aware Mobile Community-based VoD streaming solution over vehicular ad hoc networks (PMCV). PMCV relies on a newly designed mobile community detection scheme and an innovative community member management mechanism. The former employs a novel fuzzy ant-inspired clustering algorithm and an innovative mobility similarity estimation model to group together the mobile users with similar behavior in terms of playback and movement into mobile communities. The latter introduces the role and task of members, member join and leave, collaborative store and search for resources, and replacement of a broker member. Simulation-based testing shows how PMCV outperforms another state-of-the-art solution in terms of performance.

Median evidential c-means algorithm and its application to community detection

Median clustering is of great value for partitioning relational data. In this paper, a new prototype-based clustering method, called Median Evidential C-Means (MECM), which is an extension of median c-means and median fuzzy c-means on the theoretical framework of belief functions is proposed. The median variant relaxes the restriction of a metric space embedding for the objects but constrains the prototypes to be in the original data set. Due to these properties, MECM could be applied to graph clustering problems. A community detection scheme for social networks based on MECM is investigated and the obtained credal partitions of graphs, which are more refined than crisp and fuzzy ones, enable us to have a better understanding of the graph structures. An initial prototype-selection scheme based on evidential semi-centrality is presented to avoid local premature convergence and an evidential modularity function is defined to choose the optimal number of communities. Finally, experiments in synthetic and real data sets illustrate the performance of MECM and show its difference to other methods.

User communities evolution in microblogs: A public awareness barometer for real world events

In social media, users' interactions are affected by real-world events which influence emergence and shifts of opinions and topics. Interactions around an event-related topic can be captured in a weighted network, while identification of connectivity and intensity patterns can improve understanding of users' interest on the topic. Community detection is studied here as a means to reveal groups of social media users with common interaction patterns in such networks. The proposed community detection approach identifies communities exploiting both structural properties and intensity patterns, while dynamics of communities' evolution around an event are revealed based on an iterative community detection and mapping scheme. We investigate the importance of considering interactions' intensity for community detection via a benchmarking process on synthetic graphs and propose a generic framework for: i) modeling user interactions, ii) identifying static and evolving communities around events, iii) extracting quantitative and qualitative measurements from the communities' timeline, iv) leveraging measurements to understand the events' impact. Two real-world case studies based on Twitter interactions demonstrate the framework's potential for capturing and interpreting associations among communities and events.