X-Rank: Explainable Ranking in Complex Multi-Layered Networks
X-Rank: Explainable Ranking in Complex Multi-Layered Networks
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DOI：
10.1145/3269206.3269224
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发表时间：
2018-10
期刊：
影响因子：
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通讯作者：
Jian Kang;Scott Freitas;Haichao Yu;Yinglong Xia;Nan Cao;Hanghang Tong
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文献类型：
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作者：
Jian Kang;Scott Freitas;Haichao Yu;Yinglong Xia;Nan Cao;Hanghang Tong
在本文中，我们提出了一个基于Web的原型，在多层网络的可解释的排名算法，结合网络拓扑结构和知识信息。虽然传统的排名算法（如PageRank和HITS）是探索网络底层结构的重要工具，但它们在生成高精度排名方面存在两个基本限制。首先，它们主要集中在网络拓扑上，忽略了其他信息源（例如属性，知识）。其次，大多数算法不向最终用户解释为什么算法给出特定的排名结果，这阻碍了排名信息的可用性。我们开发了Xrank，一个可解释的排名工具，以解决这些缺点。实证结果表明，我们的可解释的排名方法不仅提高了排名的准确性，但有利于用户的排名，通过探索在多层网络的顶部影响因素的理解。基于Web的原型（Xrank：http://www.x-rank.net）目前已上线-我们相信它将帮助研究人员和从业者探索和利用多层网络数据。
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.