Venue2Vec: An Efficient Embedding Model for Fine-Grained User Location Prediction in Geo-Social Networks

The prevalence of Location-Based Social Networks (LBSNs) significantly improves the location-aware capability of services by providing Geo-tagged information. Relied on a great number of user check-in data in the location-based social networks, their essential mobility modes are able to be comprehensively studied, which is basic for forecasting the next venue where a specific user is going to visit considering his relevant historical check-in data. Since there exist different kinds of nodes and interactions between nodes, these information could be look upon as a network that is made up of heterogeneous information. In this network a few of different semantic meta paths could be obtained. Enlightened from the competitive advantage of embedding method relied upon meta-path contexts in the heterogeneous information network, we study a joint deep learning scheme exploring different meta-path context information to forecast fine-grained location. In order to capture different semantics in a user-location interaction, we adopt a simple but high-efficient attention method to learn the meta-path importance or weights. In the terms of model optimization, considering we have only positive sample data and there exists intrinsically latent feedback in check-in information, herein a pairwise learning method is utilized for maximizing the margin between visited and invisible venues. Experiment in different data-sets validate the competitive performance of the suggested approach under different assessment criterion.

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A Label Propagation Based User Locations Prediction Algorithm in Social Network

Communications in Computer and Information Science - Cyber Security ◽

10.1007/978-981-33-4922-3_12 ◽

2020 ◽

pp. 165-174

Author(s):

Huan Ma ◽

Wei Wang

Keyword(s):

Social Networks ◽

Social Network ◽

Community Detection ◽

Label Propagation ◽

Prediction Algorithm ◽

Location Prediction ◽

Network Graph ◽

Propagation Algorithm ◽

Node Location ◽

User Location

AbstractNetwork community detection is an important service provided by social networks, and social network user location can greatly improve the quality of community detection. Label propagation is one of the main methods to realize the user location prediction. The traditional label propagation algorithm has the problems including “location label countercurrent” and the update randomness of node location label, which seriously affects the accuracy of user location prediction. In this paper, a new location prediction algorithm for social networks based on improved label propagation algorithm is proposed. By computing the K-hop public neighbor of any two point in the social network graph, the nodes with the maximal similarity and their K-hopping neighbors are merged to constitute the initial label propagation set. The degree of nodes not in the initial set are calculated. The node location labels are updated asynchronously is adopted during the iterative process, and the node with the largest degree is selected to update the location label. The improvement proposed solves the “location label countercurrent” and reduces location label updating randomness. The experimental results show that the proposed algorithm improves the accuracy of position prediction and reduces the time cost compared with the traditional algorithms.

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Privacy-Enhancing k-Nearest Neighbors Search over Mobile Social Networks

Sensors ◽

10.3390/s21123994 ◽

2021 ◽

Vol 21 (12) ◽

pp. 3994

Author(s):

Yuxi Li ◽

Fucai Zhou ◽

Yue Ge ◽

Zifeng Xu

Keyword(s):

Social Networks ◽

Access Control ◽

Location Privacy ◽

Nearest Neighbors ◽

Search Pattern ◽

Broadcast Encryption ◽

Mobile Social Networks ◽

K Nearest Neighbors ◽

Fine Grained ◽

Server Architecture

Focusing on the diversified demands of location privacy in mobile social networks (MSNs), we propose a privacy-enhancing k-nearest neighbors search scheme over MSNs. First, we construct a dual-server architecture that incorporates location privacy and fine-grained access control. Under the above architecture, we design a lightweight location encryption algorithm to achieve a minimal cost to the user. We also propose a location re-encryption protocol and an encrypted location search protocol based on secure multi-party computation and homomorphic encryption mechanism, which achieve accurate and secure k-nearest friends retrieval. Moreover, to satisfy fine-grained access control requirements, we propose a dynamic friends management mechanism based on public-key broadcast encryption. It enables users to grant/revoke others’ search right without updating their friends’ keys, realizing constant-time authentication. Security analysis shows that the proposed scheme satisfies adaptive L-semantic security and revocation security under a random oracle model. In terms of performance, compared with the related works with single server architecture, the proposed scheme reduces the leakage of the location information, search pattern and the user–server communication cost. Our results show that a decentralized and end-to-end encrypted k-nearest neighbors search over MSNs is not only possible in theory, but also feasible in real-world MSNs collaboration deployment with resource-constrained mobile devices and highly iterative location update demands.

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Efficient Vector Partitioning Algorithms for Graph Clustering

journal of Data Intelligence ◽

10.26421/jdi1.2-1 ◽

2020 ◽

Vol 1 (2) ◽

pp. 101-123

Author(s):

Hiroaki Shiokawa ◽

Yasunori Futamura

Keyword(s):

Social Networks ◽

Large Scale ◽

Clustering Algorithm ◽

Ground Truth ◽

Graph Clustering ◽

Mining Communities ◽

Fine Grained ◽

Efficient Vector ◽

Public Datasets ◽

Many Core

This paper addressed the problem of finding clusters included in graph-structured data such as Web graphs, social networks, and others. Graph clustering is one of the fundamental techniques for understanding structures present in the complex graphs such as Web pages, social networks, and others. In the Web and data mining communities, the modularity-based graph clustering algorithm is successfully used in many applications. However, it is difficult for the modularity-based methods to find fine-grained clusters hidden in large-scale graphs; the methods fail to reproduce the ground truth. In this paper, we present a novel modularity-based algorithm, \textit{CAV}, that shows better clustering results than the traditional algorithm. The proposed algorithm employs a cohesiveness-aware vector partitioning into the graph spectral analysis to improve the clustering accuracy. Additionally, this paper also presents a novel efficient algorithm \textit{P-CAV} for further improving the clustering speed of CAV; P-CAV is an extension of CAV that utilizes the thread-based parallelization on a many-core CPU. Our extensive experiments on synthetic and public datasets demonstrate the performance superiority of our approaches over the state-of-the-art approaches.

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