Geometric Mesh Partitioning: Implementation and Experiments
SIAM Journal on Scientific Computing
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Finding community structure in mega-scale social networks: [extended abstract]
Proceedings of the 16th international conference on World Wide Web
On the evolution of user interaction in Facebook
Proceedings of the 2nd ACM workshop on Online social networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
EigenSpokes: surprising patterns and scalable community chipping in large graphs
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Revealing network communities with a nonlinear programming method
Information Sciences: an International Journal
Understanding and improving relational matrix factorization in recommender systems
Proceedings of the 7th ACM conference on Recommender systems
Online community detection for large complex networks
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
A spectral approach to detecting subtle anomalies in graphs
Journal of Intelligent Information Systems
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Different from Laplacian or normal matrix, the properties of the adjacency eigenspace received much less attention. Recent work showed that nodes projected into the adjacency eigenspace exhibit an orthogonal line pattern and nodes from the same community locate along the same line. In this paper, we conduct theoretical studies based on graph perturbation to demonstrate why this line orthogonality property holds in the adjacency eigenspace and why it generally disappears in the Laplacian and normal eigenspaces. Using the orthogonality property in the adjacency eigenspace, we present a graph partition algorithm, AdjCluster, which first projects node coordinates to the unit sphere and then applies the classic k-means to find clusters. Empirical evaluations on synthetic data and real-world social networks validate our theoretical findings and show the effectiveness of our graph partition algorithm.