The graphlet spectrum

Kondor, Risi; Shervashidze, Nino; Borgwardt, Karsten

doi:10.1145/1553374.1553443

Cited by 60 publications

(57 citation statements)

References 16 publications

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“…Typical applications include automated reasoning [36], bioinformatics/chemoinformatics [37, 38]. Generally speaking, graph kernels can be categorized into three classes: kernels based on walks [10, 11, 39, 40, 41], kernels based on limited-sized subgraphs [42, 43, 44] and kernels based on subtree patterns [45, 46, 47]. Graph kernels based on random walk is one of the most successful choices [48].…”

Section: Related Workmentioning

confidence: 99%

Enhancing Team Composition in Professional Networks: Problem Definitions and Fast Solutions

Tong

Cao

et al. 2017

IEEE Trans. Knowl. Data Eng.

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In this paper, we study ways to enhance the composition of teams based on new requirements in a collaborative environment. We focus on recommending team members who can maintain the team’s performance by minimizing changes to the team’s skills and social structure. Our recommendations are based on computing team-level similarity, which includes skill similarity, structural similarity as well as the synergy between the two. Current heuristic approaches are one-dimensional and not comprehensive, as they consider the two aspects independently. To formalize team-level similarity, we adopt the notion of graph kernel of attributed graphs to encompass the two aspects and their interaction. To tackle the computational challenges, we propose a family of fast algorithms by (a) designing effective pruning strategies, and (b) exploring the smoothness between the existing and the new team structures. Extensive empirical evaluations on real world datasets validate the effectiveness and efficiency of our algorithms.

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Section: Related Workmentioning

confidence: 99%

Enhancing Team Composition in Professional Networks: Problem Definitions and Fast Solutions

Tong

Cao

et al. 2017

IEEE Trans. Knowl. Data Eng.

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“…Kernels for discrete objects have been studied by Haussler [14], and many graph kernels have been proposed after that. There are three major groups in graph kernels: kernels based on walks and paths [9,20,10,3,42,43], kernels based on limited-size subgraphs [16,36,21], and kernels based on subtree patterns [25,35,15]. Among them, random walk based kernels have been proved to be one of the most successful methods [5,4,34].…”

Section: Related Workmentioning

confidence: 99%

Fast Random Walk Graph Kernel

Kang¹,

Tong²,

Sun³

2012

Proceedings of the 2012 SIAM International Conference on Data Mining

104

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Random walk graph kernel has been used as an important tool for various data mining tasks including classification and similarity computation. Despite its usefulness, however, it suffers from the expensive computational cost which is at least O(n 3 ) or O(m 2 ) for graphs with n nodes and m edges.In this paper, we propose Ark, a set of fast algorithms for random walk graph kernel computation. Ark is based on the observation that real graphs have much lower intrinsic ranks, compared with the orders of the graphs. Ark exploits the low rank structure to quickly compute random walk graph kernels in O(n 2 ) or O(m) time.Experimental results show that our method is up to 97,865× faster than the existing algorithms, while providing more than 91.3% of the accuracies.

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“…We notice a few recent studies that are moving towards the direction of defining the relationship among features in graphs and sets. For example in the recently defined graph Graphlet Spectrum kernel [19], the spatial relationship of graph feature (called graphlets) are explored in an algebraic framework for measuring the structure similarity of graph adjacency matrices. In addition, recently developed association net uses a graph model to represent a set of association rules [29].…”

Section: Feature Graph Constructionmentioning

confidence: 99%

Boosting with structure information in the functional space

Fei

Huan

2010

Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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Boosting is a very successful classification algorithm that produces a linear combination of "weak" classifiers (a.k.a. base learners) to obtain high quality classification models. In this paper we propose a new boosting algorithm where base learners have structure relationships in the functional space. Though such relationships are generic, our work is particularly motivated by the emerging topic of pattern based classification for semi-structured data including graphs. Towards an efficient incorporation of the structure information, we have designed a general model where we use an undirected graph to capture the relationship of subgraph-based base learners. In our method, we combine both L1 norm and Laplacian based L2 norm penalty with Logit loss function of Logit Boost. In this approach, we enforce model sparsity and smoothness in the functional space spanned by the basis functions. We have derived efficient optimization algorithms based on coordinate decent for the new boosting formulation and theoretically prove that it exhibits a natural grouping effect for nearby spatial or overlapping features. Using comprehensive experimental study, we have demonstrated the effectiveness of the proposed learning methods.

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The graphlet spectrum

Cited by 60 publications

References 16 publications

Enhancing Team Composition in Professional Networks: Problem Definitions and Fast Solutions

Enhancing Team Composition in Professional Networks: Problem Definitions and Fast Solutions

Fast Random Walk Graph Kernel

Boosting with structure information in the functional space

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