Scatter/Gather: a cluster-based approach to browsing large document collections
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Grouper: a dynamic clustering interface to Web search results
WWW '99 Proceedings of the eighth international conference on World Wide Web
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Object-level ranking: bringing order to Web objects
WWW '05 Proceedings of the 14th international conference on World Wide Web
Improving web search results using affinity graph
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Spectral clustering for multi-type relational data
ICML '06 Proceedings of the 23rd international conference on Machine learning
Relational Dependency Networks
The Journal of Machine Learning Research
Classification in Networked Data: A Toolkit and a Univariate Case Study
The Journal of Machine Learning Research
Hypergraph spectral learning for multi-label classification
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Ranking-based clustering of heterogeneous information networks with star network schema
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Combining link and content for community detection: a discriminative approach
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
A general markov framework for page importance computation
Proceedings of the 18th ACM conference on Information and knowledge management
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Graph regularized transductive classification on heterogeneous information networks
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
Mining heterogeneous information networks: the next frontier
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Query-driven discovery of semantically similar substructures in heterogeneous networks
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient label propagation for classification on information networks
Proceedings of the Third Symposium on Information and Communication Technology
Mining knowledge from interconnected data: a heterogeneous information network analysis approach
Proceedings of the VLDB Endowment
Proceedings of the 20th ACM international conference on Multimedia
MultiAspectForensics: mining large heterogeneous networks using tensor
International Journal of Web Engineering and Technology
Research-insight: providing insight on research by publication network analysis
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Mining heterogeneous information networks: a structural analysis approach
ACM SIGKDD Explorations Newsletter
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Multi-label relational neighbor classification using social context features
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Social influence based clustering of heterogeneous information networks
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
On the utility of abstraction in labeling actors in social networks
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Information graph model and application to online advertising
Proceedings of the 1st workshop on User engagement optimization
Learning latent representations of nodes for classifying in heterogeneous social networks
Proceedings of the 7th ACM international conference on Web search and data mining
On building entity recommender systems using user click log and freebase knowledge
Proceedings of the 7th ACM international conference on Web search and data mining
Personalized entity recommendation: a heterogeneous information network approach
Proceedings of the 7th ACM international conference on Web search and data mining
User behavior learning and transfer in composite social networks
ACM Transactions on Knowledge Discovery from Data (TKDD) - Casin special issue
Interesting pattern mining in multi-relational data
Data Mining and Knowledge Discovery
| Hi-index | 0.01 |
It has been recently recognized that heterogeneous information networks composed of multiple types of nodes and links are prevalent in the real world. Both classification and ranking of the nodes (or data objects) in such networks are essential for network analysis. However, so far these approaches have generally been performed separately. In this paper, we combine ranking and classification in order to perform more accurate analysis of a heterogeneous information network. Our intuition is that highly ranked objects within a class should play more important roles in classification. On the other hand, class membership information is important for determining a quality ranking over a dataset. We believe it is therefore beneficial to integrate classification and ranking in a simultaneous, mutually enhancing process, and to this end, propose a novel ranking-based iterative classification framework, called RankClass. Specifically, we build a graph-based ranking model to iteratively compute the ranking distribution of the objects within each class. At each iteration, according to the current ranking results, the graph structure used in the ranking algorithm is adjusted so that the sub-network corresponding to the specific class is emphasized, while the rest of the network is weakened. As our experiments show, integrating ranking with classification not only generates more accurate classes than the state-of-art classification methods on networked data, but also provides meaningful ranking of objects within each class, serving as a more informative view of the data than traditional classification.