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.
Sun and al. @cite_24 @cite_28 in the context of a bi-typed network (for example a bipartite bibliographic graph with only authors and conference venues) or star-typed networks (for example a bibliographic graph where we have papers and all the other features such as authors, conference venues, terms, are linked via papers) propose a ranking schema combined with clustering, where the clustering algorithm improves the ranking and vice-versa. One of the ranking function proposed is similar to ours but applies only to the simpler graphs described above with only two types of nodes. @cite_2 the authors, still in the context of bibliographic data, proposed a model similar to one of our models, namely the Simple Heap model . It mainly differs from ours for the weighting strategy and the use of a non-static model. However we consider an enriched structure with a complete set of relations between features. For example in the contest of bibliographic data we enrich the graph adding weighed links between authors, journals, and subject classification.
@cite_24 @cite_28 @cite_2
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The problem of predicting links or interactions between objects in a network, is an important task in network analysis. Along this line, link prediction between co-authors in a co-author network is a frequently studied problem. In most of these studies, authors are considered in a homogeneous network, .e., only one type of objects(author type) and one type of links (co-authorship) exist in the network. However, in a real bibliographic network, there are multiple types of objects ( .g., venues, topics, papers) and multiple types of links among these objects. In this paper, we study the problem of co-author relationship prediction in the heterogeneous bibliographic network, and a new methodology called, .e., meta path-based relationship prediction model, is proposed to solve this problem. First, meta path-based topological features are systematically extracted from the network. Then, a supervised model is used to learn the best weights associated with different topological features in deciding the co-author relationships. We present experiments on a real bibliographic network, the DBLP network, which show that metapath-based heterogeneous topological features can generate more accurate prediction results as compared to homogeneous topological features. In addition, the level of significance of each topological feature can be learned from the model, which is helpful in understanding the mechanism behind the relationship building. A model is presented of the manifestation of the birth and development of a scientific specialty in a collection of journal papers. The proposed model, Cumulative Advantage by Paper with Exemplars (CAPE) is an adaptation of Price's cumulative advantage model (D. Price, [1976]. Two modifications are made: (a) references are cited in groups by paper, and (b) the model accounts for the generation of highly cited exemplar references immediately after the birth of the specialty. This simple growth process mimics many characteristic features of real collections of papers, including the structure of the paper-to-reference matrix, the reference-per-paper distribution, the paper-per-reference distribution, the bibliographic coupling distribution, the cocitation distribution, the bibliographic coupling clustering coefficient distribution, and the temporal distribution of exemplar references. The model yields a great deal of insight into the process that produces the connectedness and clustering of a collection of articles and references. Two examples are presented and successfully modeled: a collection of 131 articles on MEMS RF (microelectromechnical systems radio frequency) switches, and a collection of 901 articles on the subject of complex networks. © 2005 Wiley Periodicals, Inc. We describe a methodology to examine bipartite relational data structures as exemplified in networks of corporate interlocking. These structures can be represented as bipartite graphs of directors and companies, but direct comparison of empirical datasets is often problematic because graphs have different numbers of nodes and different densities. We compare empirical bipartite graphs to simulated random graph distributions conditional on constraints implicit in the observed datasets. We examine bipartite graphs directly, rather than simply converting them to two 1-mode graphs, allowing investigation of bipartite statistics important to connection redundancy and bipartite connectivity. We introduce a new bipartite clustering coefficient that measures tendencies for localized bipartite cycles. This coefficient can be interpreted as an indicator of inter-company and inter-director closenesss but high levels of bipartite clustering have a cost for long range connectivity. We also investigate degree distributions, path lengths, and counts of localized subgraphs. Using this new approach, we compare global structural properties of US and Australian interlocking company directors. By comparing observed statistics against those from the simulations, we assess how the observed graphs are structured, and make comparisons between them relative to the simulated graph distributions. We conclude that the two networks share many similarities and some differences. Notably, both structures tend to be influenced by the clustering of directors on boards, more than by the accumulation of board seats by individual directorss that shared multiple board memberships (multiple interlocks) are an important feature of both infrastructures, detracting from global connectivity (but more so in the Australian case)s and that company structural power may be relatively more diffuse in the US structure than in Australia. In this paper, we present the results of a user study on exploratory search activities in a social science digital library. We conducted a user study with 32 participants with a social sciences background—16 postdoctoral researchers and 16 students—who were asked to solve a task on searching related work to a given topic. The exploratory search task was performed in a 10-min time slot. The use of certain search activities is measured and compared to gaze data recorded with an eye tracking device. We use a novel tree graph representation to visualise the users’ search patterns and introduce a way to combine multiple search session trees. The tree graph representation is capable of creating one single tree for multiple users and identifying common search patterns. In addition, the information behaviour of students and postdoctoral researchers is being compared. The results show that search activities on the stratagem level are frequently utilised by both user groups. The most heavily used search activities were keyword search, followed by browsing through references and citations, and author searching. The eye tracking results showed an intense examination of documents metadata, especially on the level of citations and references. When comparing the group of students and postdoctoral researchers, we found significant differences regarding gaze data on the area of the journal name of the seed document. In general, we found a tendency of the postdoctoral researchers to examine the metadata records more intensively with regard to dwell time and the number of fixations. By creating combined session trees and deriving subtrees from those, we were able to identify common patterns like economic (explorative) and exhaustive (navigational) behaviour. Our results show that participants utilised multiple search strategies starting from the seed document, which means that they examined different paths to find related publications.