After the phenomenal success of the PageRank algorithm, many researchers have extended the PageRank approach to ranking graphs with richer structures in addition to the simple linkage structure. Indeed, in some scenarios we have to deal with networks modeling multi-parameters data where each node has additional features and there are important relationships between such features.This paper addresses the need of a systematic approach to deal with multi-parameter data. We propose models and ranking algorithms that can be applied to a large variety of networks (bibliographic data, patent data, twitter and social data, healthcare data). We focus on several aspects not previously addressed in the literature: (1) we propose different models for ranking multi-parameters data and a class of numerical algorithms for efficiently computing the ranking score of such models, (2) we analyze stability and convergence of the proposed numerical schemes and we derive a fast and stable ranking algorithm, (3) we analyze the robustness of our models when data are incomplete. The comparison of the rank on the incomplete data with the rank on the full structure shows that our models compute consistent rankings whose correlation is up to 60 when just 10 of the links of the attributes are maintained.
Another contribution of this paper is the investigation of adequate numerical techniques to compute the ranking score. In particular, in we show how the computation of the ranks relies upon the solution of a structured linear system and in we discuss and compare the different algorithms which can be used to solve that system. Dealing with big data requires indeed particular care in the choice of the numerical methods used in the algorithms that should be stabile and fast. The final algorithm (Procedure SystemSolver in ) has been chosen on the basis of several tests aiming to validate its properties of convergence and stability. A similar analysis has not been done in the literature, and often even methods requiring matrix manipulations @cite_28 or spectral algorithms @cite_2 miss to analyze this important aspect.
@cite_28 @cite_2
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A heterogeneous information network is an information network composed of multiple types of objects. Clustering on such a network may lead to better understanding of both hidden structures of the network and the individual role played by every object in each cluster. However, although clustering on homogeneous networks has been studied over decades, clustering on heterogeneous networks has not been addressed until recently. A recent study proposed a new algorithm, RankClus, for clustering on bi-typed heterogeneous networks. However, a real-world network may consist of more than two types, and the interactions among multi-typed objects play a key role at disclosing the rich semantics that a network carries. In this paper, we study clustering of multi-typed heterogeneous networks with a star network schema and propose a novel algorithm, NetClus, that utilizes links across multityped objects to generate high-quality net-clusters. An iterative enhancement method is developed that leads to effective ranking-based clustering in such heterogeneous networks. Our experiments on DBLP data show that NetClus generates more accurate clustering results than the baseline topic model algorithm PLSA and the recently proposed algorithm, RankClus. Further, NetClus generates informative clusters, presenting good ranking and cluster membership information for each attribute object in each net-cluster. In this paper, we present a novel approach that models the mutual reinforcing relationship among papers, authors and publication venues with due cognizance of publication time. We further integrate bookmark information which models the relationship between users' expertise and papers' quality into the composite citation network using random walk with restart framework. The experimental results with ACM dataset show that 1) the proposed method outperforms the traditional methods; 2) by incorporating the temporal factor, the ranking result of latest publications can be greatly improved; 3) the integration of user generated content further enhances the ranking result.