In this paper we present a web-based prototype for an explainable ranking algorithm in multi-layered networks, incorporating both network topology and knowledge information. While traditional ranking algorithms such as PageRank and HITS are important tools for exploring the underlying structure of networks, they have two fundamental limitations in their efforts to generate high accuracy rankings. First, they are primarily focused on network topology, leaving out additional sources of information (e.g. attributes, knowledge). Secondly, most algorithms do not provide explanations to the end-users on why the algorithm gives the specific ranking results, hindering the usability of the ranking information. We developed Xrank, an explainable ranking tool, to address these drawbacks. Empirical results indicate that our explainable ranking method not only improves ranking accuracy, but facilitates user understanding of the ranking by exploring the top influential elements in multi-layered networks. The web-based prototype (Xrank: http://www.x-rank.net) is currently online - we believe it will assist both researchers and practitioners looking to explore and exploit multi-layered network data.
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AURORA: Auditing PageRank on Large Graphs
Ranking on large-scale graphs plays a fundamental role in many high-impact application domains, ranging from information retrieval, recommender systems, sports team management, biology to neuroscience and many more. PageRank, together with many of its random walk based variants, has become one of the most well-known and widely used algorithms, due to its mathematical elegance and the superior performance across a variety of application domains. Important as it might be, state-of-the-art lacks an intuitive way to explain the ranking results by PageRank (or its variants), e.g., why it thinks the returned top-k webpages are the most important ones in the entire graph; why it gives a higher rank to actor John than actor Smith in terms of their relevance w.r.t. a particular movie? In order to answer these questions, this paper proposes a paradigm shift for PageRank, from identifying which nodes are most important to understanding why the ranking algorithm gives a particular ranking result. We formally define the PageRank auditing problem, whose central idea is to identify a set of key graph elements (e.g., edges, nodes, subgraphs) with the highest influence on the ranking results. We formulate it as an opti-mization problem and propose a family of effective and scalable algorithms (Aurora) to solve it. Our algorithms measure the influence of graph elements and incrementally select influential elements w.r.t. their gradients over the ranking results. We perform extensive empirical evaluations on real-world datasets, which demonstrate that the proposed methods (Aurora) provide intuitive explanations with a linear scalability.
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- NSF-PAR ID:
- 10099227
- Date Published:
- Journal Name:
- 2018 IEEE International Conference on Big Data (Big Data)
- Page Range / eLocation ID:
- 713 to 722
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/BigData.2018.8622563
