A Multilinear Singular Value Decomposition
SIAM Journal on Matrix Analysis and Applications
Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals
Data Mining and Knowledge Discovery
Extracting evolution of web communities from a series of web archives
Proceedings of the fourteenth ACM conference on Hypertext and hypermedia
Exploratory Social Network Analysis with Pajek
Exploratory Social Network Analysis with Pajek
Relation between PLSA and NMF and implications
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Sparse Image Coding Using a 3D Non-Negative Tensor Factorization
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Non-negative tensor factorization with applications to statistics and computer vision
ICML '05 Proceedings of the 22nd international conference on Machine learning
Algorithm 862: MATLAB tensor classes for fast algorithm prototyping
ACM Transactions on Mathematical Software (TOMS)
Combining content and link for classification using matrix factorization
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
An event-based framework for characterizing the evolutionary behavior of interaction graphs
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Topic modeling with network regularization
Proceedings of the 17th international conference on World Wide Web
Learning multiple graphs for document recommendations
Proceedings of the 17th international conference on World Wide Web
Facetnet: a framework for analyzing communities and their evolutions in dynamic networks
Proceedings of the 17th international conference on World Wide Web
Probabilistic polyadic factorization and its application to personalized recommendation
Proceedings of the 17th ACM conference on Information and knowledge management
FacetCube: a framework of incorporating prior knowledge into non-negative tensor factorization
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Community detection in Social Media
Data Mining and Knowledge Discovery
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As the amount of noisy, unorganized, linked data on the Internet increases dramatically, how to efficiently analyze such data becomes a challenging research problem. In this paper, we propose a framework, iOLAP, that offers functionalities for analyzing networked data from Internet, social networks, scientific paper citations, etc. We first identify four main data dimensions that are common in most of networked data, namely people, relation, content, and time. Motivated by the fact that various dimensions of data jointly affect each other, we propose a polyadic factorization approach to directly model all the dimensions simultaneously in a unified framework. We provide detailed theoretical analysis of the new modeling framework. In addition to the theoretical framework, we also present an efficient implementation of the algorithm that takes advantage of the sparseness of data and has time complexity linear in the number of data records in a dataset. We then apply the proposed models to analyzing the blogosphere and personalizing recommendation in paper citations. Extensive experimental studies showed that our framework is able to provide deep insights jointed obtained from various dimensions of networked data.