Imperial College London

DrWeirenYu

Faculty of EngineeringDepartment of Computing

Honorary Research Fellow
 
 
 
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Contact

 

weiren.yu Website

 
 
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Location

 

Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Publication Type
Year
to

17 results found

Yu W, Mccann, 2015, Effectively Positioning Water Loss Event in Smart Water Networks, 2nd International Electronic Conference on Sensors and Applications, Publisher: MDPI, ISSN: 1424-8220

Conference paper

Yu W, McCann J, 2015, Co-Simmate: Quick Retrieving All Pairwise Co-Simrank Relevance, The 53rd Annual Meeting of the Association for Computational Linguistics

Co-Simrank is a useful Simrank-like measureof similarity based on graph structure.The existing method iteratively computeseach pair of Co-Simrank score from a dotproduct of two Pagerank vectors, entailingO(log(1/ǫ)n3) time to compute all pairsof Co-Simranks in a graph with n nodes,to attain a desired accuracy ǫ. In this study,we devise a model, Co-Simmate, to speedup the retrieval of all pairs of Co-Simranksto O(log2(log(1/ǫ))n3) time. Moreover,we show the optimality of Co-Simmateamong other hop-(uk) variations, and integrateit with a matrix decomposition basedmethod on singular graphs to attain higherefficiency. The viable experiments verifythe superiority of Co-Simmate to others.

Conference paper

Yu W, McCann J, 2015, High Quality Graph-Based Similarity Retrieval on Large Graphs, The 38th ACM SIGIR International Conference, Publisher: ACM

Conference paper

Yu W, Lin X, Zhang W, McCann Jet al., 2015, Fast All-Pairs SimRank Assessment on Large Graphs and Bipartite Domains, IEEE Transactions on Knowledge and Data Engineering, Vol: 27, Pages: 1810-1823, ISSN: 1041-4347

SimRank is a powerful model for assessing vertex-pair similarities in a graph. It follows the concept that two vertices are similar if they are referenced by similar vertices. The prior work [18] exploits partial sums memoization to compute SimRankin O(Kmn) time on a graph of n vertices and m edges, for K iterations. However, the computations among different partial sums may have duplicate redundancy. Besides, to guarantee a given accuracy ϵ, the existing SimRank needs K = ⌈logC ϵ⌉iterations, where C is a damping factor, but the geometric rate of convergence is slow if a high accuracy is expected. In this paper, (1) a novel clustering strategy is proposed to eliminate duplicate computations occurring in partial sums, and an efficient algorithm is then devised to accelerate SimRank computation to O(Kd′n2) time, where d′ is typically much smaller than m n . (2) A new differential SimRank equation is proposed, which can represent the SimRank matrix as an exponential sum of transition matrices, as opposed to the geometric sum of the conventional counterpart. This leads to a further speedup in the convergence rate of SimRank iterations. (3) In bipartite domains, a novel finer-grained partial max clustering method is developed to speed up the computation of the Minimax SimRank variation from O(Kmn) to O(Km′n) time, where m′ (≤ m) is the number of edges ina reduced graph after edge clustering, which can be typically much smaller than m. Using real and synthetic data, we empirically verify that (1) our approach of partial sums sharing outperforms the best known algorithm by up to one order of magnitude; (2) the revised notion of SimRank further achieves a 5X speedup on large graphs while also fairly preserving the relative order of original SimRank scores; (3) our finer-grained partial max memoization for the Minimax SimRank variation in bipartite domains is 0.5–1.2 orders of magnitude faster than the baselines.

Journal article

Yu W, McCann J, 2015, Efficient partial-pairs SimRank search on large graphs, Proceedings of the VLDB Endowment International Conference on Very Large Data Bases, Vol: 8, Pages: 569-580, ISSN: 2150-8097

The assessment of node-to-node similarities based on graph topology arises in a myriad of applications, e.g., web search. SimRank is a notable measure of this type, with the intuition that "two nodes are similar if their in-neighbors are similar". While most existing work retrieving SimRank only considers all-pairs SimRank s(*, *) and single-source SimRank s(*, j) (scores between every node and query j), there are appealing applications for partial-pairs SimRank, e.g., similarity join. Given two node subsets A and B in a graph, partial-pairs SimRank assessment aims to retrieve only {s(a, b)}∀aεA,∀bεB. However, the best-known solution appears not self-contained since it hinges on the premise that the SimRank scores with node-pairs in an h-go cover set must be given beforehand.This paper focuses on efficient assessment of partial-pairs SimRank in a self-contained manner. (1) We devise a novel "seed germination" model that computes partial-pairs SimRank in O(k|E| min{|A|, |B|}) time and O(|E| + k|V|) memory for k iterations on a graph of |V| nodes and |E| edges. (2) We further eliminate unnecessary edge access to improve the time of partial-pairs SimRank to O(m min{|A|, |B|}), where m ≤ min{k|E|, Δ2k}, and Δ is the maximum degree. (3) We show that our partial-pairs SimRank model also can handle the computations of all-pairs and single-source SimRanks. (4) We empirically verify that our algorithms are (a) 38x faster than the best-known competitors, and (b) memory-efficient, allowing scores to be assessed accurately on graphs with tens of millions of links.

Journal article

Yu W, McCann J, 2014, Sig-SR: SimRank Search over Singular Graphs, The 37th Annual ACM SIGIR Conference, Publisher: Association for Computing Machinery, Pages: 859-862

SimRank is an attractive structural-context measure of similaritybetween two objects in a graph. It recursively followsthe intuition that “two objects are similar if they are referencedby similar objects”. The best known matrix-basedmethod [1] for calculating SimRank, however, implies anassumption that the graph is non-singular, i.e., its adjacencymatrix is invertible. In reality, non-singular graphs are veryrare; such an assumption in [1] is too restrictive in practice.In this paper, we provide a treatment of [1], by supportingsimilarity assessment on non-invertible adjacency matrices.Assume that a singular graph G has n nodes, with r (< n)being the rank of its adjacency matrix. (1) We show thatSimRank matrix S on G has an elegant structure: S can berepresented as a rank r matrix plus a scaled identity matrix.(2) By virtue of this, an efficient algorithm over singulargraphs, Sig-SR, is proposed for calculating all-pairs SimRankin O(r(n2 + Kr2)) time for K iterations. In contrast, theonly known matrix-based algorithm that supports singulargraphs [2] needs O(r4n2) time. The experimental results onreal and synthetic datasets demonstrate the superiority ofSig-SR on singular graphs against its baselines.

Conference paper

Lv B, Yu W, Wang L, McCann Jet al., 2014, Efficient Processing Node Proximity via Random Walk with Restart, The16th Asia-Pacific Web Conference, Publisher: Springer, Pages: 542-549, ISSN: 0302-9743

Conference paper

Yu W, Lin X, Zhang W, 2014, Fast incremental SimRank on link-evolving graphs, 2014 IEEE 30th International Conference on Data Engineering (ICDE) (2014), Publisher: IEEE, Pages: 304-315

SimRank is an arresting measure of node-pair similarity based on hyperlinks. It iteratively follows the concept that 2 nodes are similar if they are referenced by similar nodes. Real graphs are often large, and links constantly evolve with small changes over time. This paper considers fast incremental computations of SimRank on link-evolving graphs. The prior approach [12] to this issue factorizes the graph via a singular value decomposition (SVD) first, and then incrementally maintains this factorization for link updates at the expense of exactness. Consequently, all node-pair similarities are estimated in O(r4n2) time on a graph of n nodes, where r is the target rank of the low-rank approximation, which is not negligibly small in practice. In this paper, we propose a novel fast incremental paradigm. (1) We characterize the SimRank update matrix ΔS, in response to every link update, via a rank-one Sylvester matrix equation. By virtue of this, we devise a fast incremental algorithm computing similarities of n2 node-pairs in O(Kn2) time for K iterations. (2) We also propose an effective pruning technique capturing the “affected areas” of ΔS to skip unnecessary computations, without loss of exactness. This can further accelerate the incremental SimRank computation to O(K(nd+|AFF|)) time, where d is the average in-degree of the old graph, and |AFF| (≤ n2) is the size of “affected areas” in ΔS, and in practice, |AFF| ≪ n2. Our empirical evaluations verify that our algorithm (a) outperforms the best known link-update algorithm [12], and (b) runs much faster than its batch counterpart when link updates are small.

Conference paper

Yu W, Lin X, Zhang W, Chang L, Pei Jet al., 2013, More is Simpler: Effectively and Efficiently Assessing Node-Pair Similarities Based on Hyperlinks, Proceedings of the VLDB Endowment International Conference on Very Large Data Bases, Vol: 7, Pages: 13-24, ISSN: 2150-8097

Similarity assessment is one of the core tasks in hyperlinkanalysis. Recently, with the proliferation of applications,e.g., web search and collaborative filtering, SimRank hasbeen a well-studied measure of similarity between two nodesin a graph. It recursively follows the philosophy that “twonodes are similar if they are referenced (have incoming edges)from similar nodes”, which can be viewed as an aggregationof similarities based on incoming paths. Despite its popularity,SimRank has an undesirable property, i.e., “zerosimilarity”:It only accommodates paths with equal lengthfrom a common “center” node. Thus, a large portion ofother paths are fully ignored. This paper attempts to remedythis issue. (1) We propose and rigorously justify SimRank*,a revised version of SimRank, which resolves suchcounter-intuitive “zero-similarity” issues while inheriting meritsof the basic SimRank philosophy. (2) We show thatthe series form of SimRank* can be reduced to a fairlysuccinct and elegant closed form, which looks even simplerthan SimRank, yet enriches semantics without sufferingfrom increased computational cost. This leads to a fixedpointiterative paradigm of SimRank* in O(Knm) time ona graph of n nodes and m edges for K iterations, which iscomparable to SimRank. (3) To further optimize SimRank*computation, we leverage a novel clustering strategy viaedge concentration. Due to its NP-hardness, we devise an ef-ficient and effective heuristic to speed up SimRank* computationto O(Knm˜ ) time, where ˜m is generally much smallerthan m. (4) Using real and synthetic data, we empiricallyverify the rich semantics of SimRank*, and demonstrate itshigh computation efficiency.

Journal article

Yu W, Lin X, 2013, IRWR:Incremental Random Walk with Restart, The 36th international ACM SIGIR conference on Research and development in information retrieval, ACM SIGIR 2013, Publisher: ACM, Pages: 1017-1020

Conference paper

Yu W, Lin X, Zhang W, 2013, Towards efficient SimRank computation on large networks, IEEE 29th International Conference on Data Engineering (ICDE), Publisher: IEEE, Pages: 601-612, ISSN: 1063-6382

SimRank has been a powerful model for assessing the similarity of pairs of vertices in a graph. It is based on the concept that two vertices are similar if they are referenced by similar vertices. Due to its self-referentiality, fast SimRank computation on large graphs poses significant challenges. The state-of-the-art work [17] exploits partial sums memorization for computing SimRank in O(Kmn) time on a graph with n vertices and m edges, where K is the number of iterations. Partial sums memorizing can reduce repeated calculations by caching part of similarity summations for later reuse. However, we observe that computations among different partial sums may have duplicate redundancy. Besides, for a desired accuracy ϵ, the existing SimRank model requires K = [logC ϵ] iterations [17], where C is a damping factor. Nevertheless, such a geometric rate of convergence is slow in practice if a high accuracy is desirable. In this paper, we address these gaps. (1) We propose an adaptive clustering strategy to eliminate partial sums redundancy (i.e., duplicate computations occurring in partial sums), and devise an efficient algorithm for speeding up the computation of SimRank to 0(Kd'n2) time, where d' is typically much smaller than the average in-degree of a graph. (2) We also present a new notion of SimRank that is based on a differential equation and can be represented as an exponential sum of transition matrices, as opposed to the geometric sum of the conventional counterpart. This leads to a further speedup in the convergence rate of SimRank iterations. (3) Using real and synthetic data, we empirically verify that our approach of partial sums sharing outperforms the best known algorithm by up to one order of magnitude, and that our revised notion of SimRank further achieves a 5X speedup on large graphs while also fairly preserving the relative order of original SimRank scores.

Conference paper

Yu W, Lin X, Zhang W, Zhang Y, Le Jet al., 2012, SimFusion+: Extending SimFusion towards efficient estimation on large and dynamic networks, SIGIR’12, Publisher: ACM, Pages: 365-374

Conference paper

Yu W, Le J, Lin X, Zhang Wet al., 2012, On the Efficiency of Estimating Penetrating Rank on Large Graphs, 24th International Conference Scientific and Statistical Database Management, Publisher: Springer, Pages: 231-249, ISSN: 0302-9743

P-Rank (Penetrating Rank) has been suggested as a useful measure of structural similarity that takes account of both incoming and outgoing edges in ubiquitous networks. Existing work often utilizes memoization to compute P-Rank similarity in an iterative fashion, which requires cubic time in the worst case. Besides, previous methods mainly focus on the deterministic computation of P-Rank, but lack the probabilistic framework that scales well for large graphs. In this paper, we propose two efficient algorithms for computing P-Rank on large graphs. The first observation is that a large body of objects in a real graph usually share similar neighborhood structures. By merging such objects with an explicit low-rank factorization, we devise a deterministic algorithm to compute P-Rank in quadratic time. The second observation is that by converting the iterative form of P-Rank into a matrix power series form, we can leverage the random sampling approach to probabilistically compute P-Rank in linear time with provable accuracy guarantees. The empirical results on both real and synthetic datasets show that our approaches achieve high time efficiency with controlled error and outperform the baseline algorithms by at least one order of magnitude.

Conference paper

Li X, Yu W, Yang B, Le Jet al., 2011, ASAP : Towards Accurate, Stable and Accelerative Penetrating-Rank Estimation on Large Graphs, 12th International Conference, WAIM 2011, Publisher: Springer, Pages: 415-429, ISSN: 0302-9743

Pervasive web applications increasingly require a measure of similarity among objects. Penetrating-Rank (P-Rank) has been one of the promising link-based similarity metrics as it provides a comprehensive way of jointly encoding both incoming and outgoing links into computation for emerging applications. In this paper, we investigate P-Rank efficiency problem that encompasses its accuracy, stability and computational time. (1) We provide an accuracy estimate for iteratively computing P-Rank. A symmetric problem is to find the iteration number K needed for achieving a given accuracy ε. (2) We also analyze the stability of P-Rank, by showing that small choices of the damping factors would make P-Rank more stable and well-conditioned. (3) For undirected graphs, we also explicitly characterize the P-Rank solution in terms of matrices. This results in a novel non-iterative algorithm, termed ASAP , for efficiently computing P-Rank, which improves the CPU time from O(n 4) to O( n 3 ). Using real and synthetic data, we empirically verify the effectiveness and efficiency of our approaches.

Conference paper

Yu W, Zhang W, Lin X, Zhang Q, Le Jet al., 2010, A space and time efficient algorithm for SimRank computation, World Wide Web, Vol: 15, Pages: 327-353, ISSN: 1386-145X

Journal article

Yu W, Lin X, Le J, 2010, Taming Computational Complexity: Efficient and Parallel SimRank Optimizations on Undirected Graphs, The 11th International Conference on Web-Age Information Management, Publisher: Springer, Pages: 280-296, ISSN: 0302-9743

SimRank has been considered as one of the promising link-based ranking algorithms to evaluate similarities of web documents in many modern search engines. In this paper, we investigate the optimization problem of SimRank similarity computation on undirected web graphs. We first present a novel algorithm to estimate the SimRank between vertices in O(n3+ Kn2) time, where n is the number of vertices, and K is the number of iterations. In comparison, the most efficient implementation of SimRank algorithm in [1] takes O(K n3 ) time in the worst case. To efficiently handle large-scale computations, we also propose a parallel implementation of the SimRank algorithm onmultiple processors. The experimental evaluations on both synthetic and real-life data sets demonstrate the better computational time and parallel efficiency of our proposed techniques.

Conference paper

Yu W, Lin X, Le J, 2010, A Space and Time Efficient Algorithm for SimRank Computation, 2010 12th International Asia-Pacific Web Conference (APWEB), Publisher: IEEE, Pages: 164-170

SimRank has been proposed to rank web documents based on a graph model on hyperlinks. The existing techniques for conducting SimRank computation adopt an iteration computation paradigm. The most efficient technique has the time complexity O(n3) with the space requirement O(n2) in the worst case for each iteration where n is the number of nodes (web documents). In this paper, we propose novel optimization techniques such that each iteration takes the time O(min{n · m,nr}) and requires space O (n + m) where m is the number of edges in a web-graph model and r ≤ log2 7. We also show that our algorithm accelerates the convergence rate of the existing techniques. Moreover, our algorithm not only reduces the time and space complexity of the existing techniques but is also I/O efficient. We conduct extensive experiments on both synthetic and real data sets to demonstrate the efficiency and effectiveness of our iteration techniques.

Conference paper

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