Maximum co-located community search in large scale social networks

Chen, Lu; Liu, Chengfei; Zhou, Rui; Li, Jianxin; Yang, Xiaochun; Wang, Bin

doi:10.14778/3231751.3231755

Cited by 94 publications

(40 citation statements)

References 46 publications

(68 reference statements)

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“…In protein-protein interaction networks, communities correspond to functional modules of interacting proteins [7]. Moreover, community detection can be applied in many other network analysis tasks, such as edge prediction, missing attributes inference [8,9] and community search [10,11].…”

Section: Introductionmentioning

confidence: 99%

NMLPA: Uncovering Overlapping Communities in Attributed Networks via a Multi-Label Propagation Approach

Huang

Wang

2019

Sensors

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With the enrichment of the entity information in the real world, many networks with attributed nodes are proposed and studied widely. Community detection in these attributed networks is an essential task that aims to find groups where the intra-nodes are much more densely connected than the inter-nodes. However, many existing community detection methods in attributed networks do not distinguish overlapping communities from non-overlapping communities when designing algorithms. In this paper, we propose a novel and accurate algorithm called Node-similarity-based Multi-Label Propagation Algorithm (NMLPA) for detecting overlapping communities in attributed networks. NMLPA first calculates the similarity between nodes and then propagates multiple labels based on the network structure and the node similarity. Moreover, NMLPA uses a pruning strategy to keep the number of labels per node within a suitable range. Extensive experiments conducted on both synthetic and real-world networks show that our new method significantly outperforms state-of-the-art methods.

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Section: Introductionmentioning

confidence: 99%

NMLPA: Uncovering Overlapping Communities in Attributed Networks via a Multi-Label Propagation Approach

Huang

Wang

2019

Sensors

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“…Ridesharing HIN H = (V, E), meta-path scheme P, walk length l, a set of passengers U , a set of drivers D, embedding vector for each node X Outputs: Similarity between the embedding vectors of driver-passenger pairs initialize X ; for i = 1 → w do for each v ∈ V do MP = RandomWalk(H , P, v, l); X = SkipGram(MP, X ); end end extract vector embedding X D , X U calculate the similarity between X D , X U according to Equation (9) return Sim(X D , X U );…”

Section: Algorithm 1: Inputsmentioning

confidence: 99%

“…Current harvesting techniques can extract different types of travel-related information from trajectories [6,7] or a social network [8][9][10] and fuse them to find a ride-share partner. Various kinds of auxiliary data (e.g., spatial dispersion, temporal duration, and movement velocity) become available in ridesharing matching systems.…”

Section: Introductionmentioning

confidence: 99%

Efficient Ridesharing Framework for Ride-matching via Heterogeneous Network Embedding

Tang

Liu

Zhao

et al. 2020

ACM Trans. Knowl. Discov. Data

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Ridesharing has attracted increasing attention in recent years, and combines the flexibility and speed of private cars with the reduced cost of fixed-line systems to benefit alleviating traffic pressure. A major issue in ridesharing is the accurate assignment of passengers to drivers, and how to maximize the number of rides shared between people being assigned to different drivers has become an increasingly popular research topic. There are two major challenges facing ride-matching: scalability and sparsity. Here, we show that network embedding drives the optimal matches between drivers and riders. Contrary to existing approaches that merely depend on the proximity between passengers and drivers, we employ a heterogeneous network to learn the latent semantics from different choices in two types of ridesharing, and extract features in terms of user trajectories and sentiment. A novel framework for ridesharing, RShareForm, which encodes not only the objects but also a variety of semantic relationships between them, is proposed. This article extends the existing skip-gram model to incorporate meta-paths over a proposed heterogeneous network. It allows diverse features to be used to search for similar participants and then ranks them to improve the quality of ride-matching. Extensive experiments on a large-scale dataset from DiDi in Chengdu, China show that by leveraging heterogeneous network embedding with meta paths, RShareForm can significantly improve the accuracy of identifying the participants for ridesharing over existing methods, including both meta-path guided similarity search methods and variants of embedding methods. CCS Concepts: • Human-centered computing → Empirical studies in ubiquitous and mobile computing;

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“…Huang et al designed a scoring function based on k-truss to measure the popularity of a given attribute in the community, and proposed the Attribute-Truss community definition (Huang and Lakshmanan 2017). Chen et al considered the constraint of users' spatial information in k-truss search named colocated community search (Chen et al 2018).…”

Section: Related Workmentioning

confidence: 99%

Forbidden Nodes Aware Community Search

Wang

Zhu

2019

AAAI

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Community search is an important problem in network analysis, which has attracted much attention in recent years. It starts with some given nodes, pays more attention to local network structures, and gets personalized resultant communities quickly. In this paper, we argue that there are many real scenarios where some nodes are not allowed to appear in the community. Then, we introduce a new concept called forbidden nodes and present a new problem of forbidden nodes aware community search to describe these scenarios.To address the above problem, three methods are proposed, i.e., k-core based FORTE (Forbidden nOdes awaRe communiTy sEarch), k-truss based FORTE and CW based FORTE, where the effects of both forbidden nodes and query nodes are thoroughly considered for each node in the resultant community. The former two methods are able to make use of popular community structures, while the latter is based on a new metric called weighted conductance. The extensive experiments conducted on real data sets demonstrate the effectiveness of the proposed methods.

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Maximum co-located community search in large scale social networks

Cited by 94 publications

References 46 publications

NMLPA: Uncovering Overlapping Communities in Attributed Networks via a Multi-Label Propagation Approach

NMLPA: Uncovering Overlapping Communities in Attributed Networks via a Multi-Label Propagation Approach

Efficient Ridesharing Framework for Ride-matching via Heterogeneous Network Embedding

Forbidden Nodes Aware Community Search

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