Centrality is an important notion in network analysis and is used to measure the degree to which network structure contributes to the importance of a node in a network. While many different centrality measures exist, most of them apply to static networks. Most networks, on the other hand, are dynamic in nature, evolving over time through the addition or deletion of nodes and edges. A popular approach to analyzing such networks represents them by a static network that aggregates all edges observed over some time period. This approach, however, under or overestimates centrality of some nodes. We address this problem by introducing a novel centrality metric for dynamic network analysis. This metric exploits an intuition that in order for one node in a dynamic network to influence another over some period of time, there must exist a path that connects the source and destination nodes through intermediaries at different times. We demonstrate on an example network that the proposed metric leads to a very different ranking than analysis of an equivalent static network. We use dynamic centrality to study a dynamic citations network and contrast results to those reached by static network analysis.
Time-aware ranking Closely related to dynamic network analysis is the problem of time-aware ranking of Web pages in information retrieval. This research is motivated by the observation @cite_22 that PageRank's Web ranking algorithm is biased against newer pages, which may not have had enough time to accumulate links to give it a high rank. Several methods have been proposed to address the recency bias in PageRank, including @cite_22 @cite_15 @cite_29 @cite_14 . In general terms, these methods weigh edges in the network by age, with newer edges contributing more heavily to a page's importance. Our motivation is different. Rather than focus on improving the rank of newer nodes, we focus instead on defining a time-aware centrality metric that takes the temporal order of edges into account.
@cite_14 @cite_15 @cite_29 @cite_22
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In web search, recency ranking refers to ranking documents by relevance which takes freshness into account. In this paper, we propose a retrieval system which automatically detects and responds to recency sensitive queries. The system detects recency sensitive queries using a high precision classifier. The system responds to recency sensitive queries by using a machine learned ranking model trained for such queries. We use multiple recency features to provide temporal evidence which effectively represents document recency. Furthermore, we propose several training methodologies important for training recency sensitive rankers. Finally, we develop new evaluation metrics for recency sensitive queries. Our experiments demonstrate the efficacy of the proposed approaches. Web search is probably the single most important application on the Internet. The most famous search techniques are perhaps the PageRank and HITS algorithms. These algorithms are motivated by the observation that a hyperlink from a page to another is an implicit conveyance of authority to the target page. They exploit this social phenomenon to identify quality pages, e.g., "authority" pages and "hub" pages. In this paper we argue that these algorithms miss an important dimension of the Web, the temporal dimension. The Web is not a static environment. It changes constantly. Quality pages in the past may not be quality pages now or in the future. These techniques favor older pages because these pages have many in-links accumulated over time. New pages, which may be of high quality, have few or no in-links and are left behind. Bringing new and quality pages to users is important because most users want the latest information. Research publication search has exactly the same problem. This paper studies the temporal dimension of search in the context of research publication search. We propose a number of methods deal with the problem. Our experimental results show that these methods are highly effective. This paper is aimed at the study of quantitative measures of the relation between Web structure, page recency, and quality of Web pages. Quality is studied using different link-based metrics considering their relationship with the structure of the Web and the last modification time of a page. We show that, as expected, Pagerank is biased against new pages. As a subproduct we propose a Pagerank variant that includes page recency into account and we obtain information on how recency is related with Web structure.