Top-k Rankings Research Articles

Scaling High-Quality Pairwise Link-Based Similarity Retrieval on Billion-Edge Graphs

SimRank is an attractive link-based similarity measure used in fertile fields of Web search and sociometry. However, the existing deterministic method by Kusumoto et al. [ 24 ] for retrieving SimRank does not always produce high-quality similarity results, as it fails to accurately obtain diagonal correction matrix D . Moreover, SimRank has a “connectivity trait” problem: increasing the number of paths between a pair of nodes would decrease its similarity score. The best-known remedy, SimRank++ [ 1 ], cannot completely fix this problem, since its score would still be zero if there are no common in-neighbors between two nodes. In this article, we study fast high-quality link-based similarity search on billion-scale graphs. (1) We first devise a “varied- D ” method to accurately compute SimRank in linear memory. We also aggregate duplicate computations, which reduces the time of [ 24 ] from quadratic to linear in the number of iterations. (2) We propose a novel “cosine-based” SimRank model to circumvent the “connectivity trait” problem. (3) To substantially speed up the partial-pairs “cosine-based” SimRank search on large graphs, we devise an efficient dimensionality reduction algorithm, PSR # , with guaranteed accuracy. (4) We give mathematical insights to the semantic difference between SimRank and its variant, and correct an argument in [ 24 ] that “if D is replaced by a scaled identity matrix (1-Ɣ)I, their top-K rankings will not be affected much”. (5) We propose a novel method that can accurately convert from Li et al. SimRank ~{S} to Jeh and Widom’s SimRank S . (6) We propose GSR # , a generalisation of our “cosine-based” SimRank model, to quantify pairwise similarities across two distinct graphs, unlike SimRank that would assess nodes across two graphs as completely dissimilar. Extensive experiments on various datasets demonstrate the superiority of our proposed approaches in terms of high search quality, computational efficiency, accuracy, and scalability on billion-edge graphs.

Unexpected-Behavior Detection Using TopK Rankings for Cybersecurity

Anomaly-based intrusion detection systems use profiles to characterize expected behavior of network users. Most of these systems characterize the entire network traffic within a single profile. This work proposes a user-level anomaly-based intrusion detection methodology using only the user’s network traffic. The proposed profile is a collection of TopK rankings of reached services by the user. To detect unexpected behaviors, the real-time traffic is organized into TopK rankings and compared to the profile using similarity measures. The experiments demonstrated that the proposed methodology was capable of detecting a particular kind of malware attack in all the users tested.

Sensitivity index to measure dependence on parameters for rankings and top-k rankings

ABSTRACTIn a multivariate framework, ranking a data set can be done by using an aggregation function in order to obtain a global score for each individual, and then by using these scores to rank the individuals. The choice of the aggregation function (e.g. a weighted sum) and the choice of the parameters of the function (e.g. the weights) may have a great influence on the obtained ranking. We introduce in this communication a ratio index that can quantify the sensitivity of the data set ranking up to a change of weights. This index is investigated in the general case and in the restricted case of top-k rankings. We also illustrate the interest to use such an index to analyse ranked data sets.

Concept and Computation of Ranking-based Dominance

We present COMPETE, a novel approach that models and computes dominance over user-provided input entities, given a database of top-k rankings. The resulting entities are found superior or inferior with tunable degree of dominance over the input set—a very intuitive, yet insightful way to explore pros and cons of entities of interest. Several notions of dominance are defined which differ in computational complexity and strictness of the dominance concept—yet, interdependent through containment relations. COMPETE is able to pick the most promising approach to satisfy a user request at minimal runtime latency, using a probabilistic model that is estimating the result sizes. The individual flavors of dominance are cast into a stack of algorithms over inverted indices and auxiliary structures. The extensive experimental evaluation over real-world and synthetically generated data and workloads demonstrates the diversity of the problem in terms of number of valid results and need for result ordering and, in particular, emphasizes the immense effect of the pruning strategies with performance gains up to an order of magnitude over baseline approaches.

Time-homogeneous top-K ranking using tensor decompositions

ABSTRACT Given N items, all having positive latent strengths, top-K ranking problem aims to identify the K items () receiving the highest ranks based on partially revealed comparisons among the items. This problem has been widely studied in the case for which comparisons are performed independently. However, identifying the top-K rankings becomes a more intricate task when comparisons per se are performed along some temporal dimension. In this paper, we investigate potential impacts of temporality on sequences of top-K items and propose a time-homogeneous ranking scheme. Our framework relies mainly on tensor decompositions, rank centrality, and an innovative continuous extension of the Bradley–Terry–Luce (BTL) model. The proposed continuous BTL model extends the win/loss nature of the logistic model to a continuous setting, further reflecting preference degrees that may exist among the compared items. Our computations, which pertain to the analysis of S&P500 data from January 2008 to December 2017, confirm that the proposed top-K ranking scheme is an effective approach to optimize cardinality-constrained portfolios which involve large volumes of noisy and incomplete data.

Measuring academic influence using heterogeneous author-citation networks

Academic influence has been traditionally measured by citation counts and metrics derived from it, such as H-index and G-index. PageRank based algorithms have been used to give higher weight to citations from more influential papers. A better metric is to add authors into the citation network so that the importance of authors and papers are evaluated recursively within the same framework. Based on such heterogeneous author-citation academic network, this paper gives a new algorithm for ranking authors. It is tested on two large networks, one in Heath domain that contains about 500 million citation links, the other in Computer Science that contains 8 million links. We find that our method outperforms other 10 methods in terms of the number of award winners identified in their top-k rankings. Surprisingly, our method can identify 8 Turing award winners among top 20 authors. It also demonstrates some interesting phenomenons. For instance, among the top authors, our ranking negatively correlates with citation ranking and paper count ranking.