Community Detection Using Spectral Clustering on Sparse Geosocial Data

Gennip, Yves van; Hunter, Blake; Ahn, Raymond; Elliott, Peter; Luh, Kyle; Halvorson, Megan; Reid, Shannon E.; Valasik, Matthew; Wo, James; Tita, George E.; Bertozzi, Andrea L.; Brantingham, P. Jeffrey

doi:10.1137/120882093

Cited by 75 publications

(54 citation statements)

References 30 publications

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“…For example, Van Gennip et al [30] create groups of users in a geo-social graph so that users in the same group are nearby and socially connected. [33] defines a distance function between two nodes that combines their social connectivity and attribute similarity.…”

Section: Graph Algorithmsmentioning

confidence: 99%

Real-Time Multi-Criteria Social Graph Partitioning

Armenatzoglou

Pham

Ntranos

et al. 2015

Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data

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Graph partitioning has attracted considerable attention due to its high practicality for real-world applications. It is particularly relevant to social networks because it enables the grouping of users into communities for market analysis and advertising purposes. In this paper, we introduce RMGP, a type of real-time multi-criteria graph partitioning for social networks that groups the users based on their connectivity and their similarity to a set of input classes. We consider RMGP as an on-line task, which may be frequently performed for different query parameters (e.g., classes). In order to overcome the serious performance issues associated with the large social graphs found in practice, we develop solutions based on a game theoretic framework. Specifically, we consider each user as a player, whose goal is to find the class that optimizes his objective function. We propose algorithms based on best-response dynamics, analyze their properties, and show their efficiency and effectiveness on real datasets under centralized and decentralized scenarios.

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Section: Graph Algorithmsmentioning

confidence: 99%

Real-Time Multi-Criteria Social Graph Partitioning

Armenatzoglou

Pham

Ntranos

et al. 2015

Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data

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“…Yu et al [39] cluster pixels considering both the RGB color vectors and spatial proximity that is useful in natural image segmentation. Gennip et al [32] use spectral clustering to identify communities in a graph where nodes are gang members and weighted edges indicate the gang members' social interactions and geographic locations. Zhang et al [40] apply clustering by adjusting the spatial distance between two objects according to the non-spatial attribute values between them.…”

Section: Related Workmentioning

confidence: 99%

Density-based place clustering in geo-social networks

Shi

Mamoulis

et al. 2014

Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data

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Spatial clustering deals with the unsupervised grouping of places into clusters and finds important applications in urban planning and marketing. Current spatial clustering models disregard information about the people who are related to the clustered places. In this paper, we show how the density-based clustering paradigm can be extended to apply on places which are visited by users of a geo-social network. Our model considers both spatial information and the social relationships between users who visit the clustered places. After formally defining the model and the distance measure it relies on, we present efficient algorithms for its implementation, based on spatial indexing. We evaluate the effectiveness of our model via a case study on real data; in addition, we design two quantitative measures, called social entropy and community score to evaluate the quality of the discovered clusters. The results show that geo-social clusters have special properties and cannot be found by applying simple spatial clustering approaches. The efficiency of our index-based implementation is also evaluated experimentally.

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“…Spectral clustering algorithm has recently gained popularity in handling many graph clustering tasks such as those reported in [1,2,3]. Compared to traditional clustering algorithms, such as k-means clustering and hierarchical clustering, spectral clustering has a very well formulated mathematical framework and is able to discover non-convex regions which may not be detected by other clustering algorithms.…”

Section: Introductionmentioning

confidence: 99%

A High Performance Implementation of Spectral Clustering on CPU-GPU Platforms

Jin

JáJá

2016

2016 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)

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Abstract-Spectral clustering is one of the most popular graph clustering algorithms, which achieves the best performance for many scientific and engineering applications. However, existing implementations in commonly used software platforms such as Matlab and Python do not scale well for many of the emerging Big Data applications. In this paper, we present a fast implementation of the spectral clustering algorithm on a CPU-GPU heterogeneous platform. Our implementation takes advantage of the computational power of the multi-core CPU and the massive multithreading and SIMD capabilities of GPUs. Given the input as data points in high dimensional space, we propose a parallel scheme to build a sparse similarity graph represented in a standard sparse representation format. Then we compute the smallest k eigenvectors of the Laplacian matrix by utilizing the reverse communication interfaces of ARPACK software and cuSPARSE library, where k is typically very large. Moreover, we implement a very fast parallelized k-means algorithm on GPUs. Our implementation is shown to be significantly faster compared to the best known Matlab and Python implementations for each step. In addition, our algorithm scales to problems with a very large number of clusters.

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Community Detection Using Spectral Clustering on Sparse Geosocial Data

Cited by 75 publications

References 30 publications

Real-Time Multi-Criteria Social Graph Partitioning

Real-Time Multi-Criteria Social Graph Partitioning

Density-based place clustering in geo-social networks

A High Performance Implementation of Spectral Clustering on CPU-GPU Platforms

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