Efficient Estimation of Heat Kernel PageRank for Local Clustering

Yang, Renchi; Xiao, Xiaokui; Wei, Zhewei; Bhowmick, Sourav S.; Zhao, Jun; Li, Ronghua

doi:10.1145/3299869.3319886

Cited by 24 publications

(31 citation statements)

References 36 publications

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“…Explored the heat kernel PageRank as a community detection algorithm, solving the exponential of the Markov matrix using a Taylor polynomial approximation. Yang et al [194]. Proposed a more efficient approach to computing heat kernel PageRank, based on Monte Carlo random walks and a reduction of the required number of random walks.…”

Section: A Overview Of Entity-oriented Search Approaches and Tasksmentioning

confidence: 99%

A Review of Graph-Based Models for Entity-Oriented Search

Devezas

Nunes

2021

SN COMPUT. SCI.

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Entity-oriented search tasks heavily rely on exploiting unstructured and structured collections. Moreover, it is frequent for text corpora and knowledge bases to provide complementary views on a common topic. While, traditionally, the retrieval unit was the document, modern search engines have evolved to also retrieve entities and to provide direct answers to the information needs of the users. Cross-referencing information from heterogeneous sources has become fundamental, however a mismatch still exists between text-based and knowledge-based retrieval approaches. The former does not account for complex relations, while the latter does not properly support keyword-based queries and ranked retrieval. Graphs are a good solution to this problem, since they can be used to represent text, entities and their relations. In this survey, we examine textbased approaches and how they evolved to leverage entities and their relations in the retrieval process. We also cover multiple aspects of graph-based models for entity-oriented search, providing an overview on link analysis and exploring graph-based text representation and retrieval, leveraging knowledge graphs for document or entity retrieval, building entity graphs from text, using graph matching for querying with subgraphs, exploiting hypergraph-based representations, and ranking based on random walks on graphs. We close with a discussion on the topic and a view of the future to motivate the research of graphbased models for entity-oriented search, particularly as joint representation models for the generalization of retrieval tasks.

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Section: A Overview Of Entity-oriented Search Approaches and Tasksmentioning

confidence: 99%

A Review of Graph-Based Models for Entity-Oriented Search

Devezas

Nunes

2021

SN COMPUT. SCI.

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“…Benchmark. We use benchmark networks to compare our local clustering algorithm with the algorithm by Chung and Simpson 21 , the TEA+ algorithm 24 , and with the results of performing a sweep on the exact heat kernel PageRank with maximum length K Benchmark networks are computer-generated graphs with ground-truth communities. We use the Lancichinetti-Fortunato-Radicchi (LFR) network as the benchmark for our numerical experiments 26 .…”

Section: Numerical Experimentsmentioning

confidence: 99%

Local clustering via approximate heat kernel PageRank with subgraph sampling

Wahlström

Nehorai

2021

Sci Rep

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Graph clustering, a fundamental technique in network science for understanding structures in complex systems, presents inherent problems. Though studied extensively in the literature, graph clustering in large systems remains particularly challenging because massive graphs incur a prohibitively large computational load. The heat kernel PageRank provides a quantitative ranking of nodes, and a local cluster can be efficiently found by performing a sweep over the heat kernel PageRank vector. But computing an exact heat kernel PageRank vector may be expensive, and approximate algorithms are often used instead. Most approximate algorithms compute the heat kernel PageRank vector on the whole graph, and thus are dependent on global structures. In this paper, we present an algorithm for approximating the heat kernel PageRank on a local subgraph. Moreover, we show that the number of computations required by the proposed algorithm is sublinear in terms of the expected size of the local cluster of interest, and that it provides a good approximation of the heat kernel PageRank, with approximation errors bounded by a probabilistic guarantee. Numerical experiments verify that the local clustering algorithm using our approximate heat kernel PageRank achieves state-of-the-art performance.

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“…They were able to obtain consistently better results for hk-relax, when compared to the push algorithm, according to the F-measure and the conductance, over multiple graphs with a varying number of nodes and edges. More recently, Yang et al [169] have also proposed the TEA and TEA+ algorithms for a more efficient computation of the heat kernel PageRank. They proposed an approximation based on Monte Carlo random walks and a secondary algorithm to help reduce the number of required random walks.…”

Section: Link Analysismentioning

confidence: 99%

“…Link Analysis [113,149,151,[162][163][164][165]169] Text as a Graph [15,16,171] Knowledge Graphs [54, 172, 174-176, 179, 185, 186, 254] Text to Entity Graph [187,189] Entity Graph to Tensor [75] Graph Matching [57,58,196,198,199] Hypergraph-Based [14,76,103,203,204] Random Walk Based [65-67, 72, 238, 239] . .…”

Section: Graph-basedmentioning

confidence: 99%

Graph-based entity-oriented search

Devezas

2021

SIGIR Forum

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Entity-oriented search has revolutionized search engines. In the era of Google Knowledge Graph and Microsoft Satori, users demand an effortless process of search. Whether they express an information need through a keyword query, expecting documents and entities, or through a clicked entity, expecting related entities, there is an inherent need for the combination of corpora and knowledge bases to obtain an answer. Such integration frequently relies on independent signals extracted from inverted indexes, and from quad indexes indirectly accessed through queries to a triplestore. However, relying on two separate representation models inhibits the effective cross-referencing of information, discarding otherwise available relations that could lead to a better ranking. Moreover, different retrieval tasks often demand separate implementations, although the problem is, at its core, the same. With the goal of harnessing all available information to optimize retrieval, we explore joint representation models of documents and entities, while taking a step towards the definition of a more general retrieval approach. Specifically, we propose that graphs should be used to incorporate explicit and implicit information derived from the relations between text found in corpora and entities found in knowledge bases. We also take advantage of this framework to elaborate a general model for entity-oriented search, proposing a universal ranking function for the tasks of ad hoc document retrieval (leveraging entities), ad hoc entity retrieval, and entity list completion. At a conceptual stage, we begin by proposing the graph-of-entity, based on the relations between combinations of term and entity nodes. We introduce the entity weight as the corresponding ranking function, relying on the idea of seed nodes for representing the query, either directly through term nodes, or based on the expansion to adjacent entity nodes. The score is computed based on a series of geodesic distances to the remaining nodes, providing a ranking for the documents (or entities) in the graph. In order to improve on the low scalability of the graph-of-entity, we then redesigned this model in a way that reduced the number of edges in relation to the number of nodes, by relying on the hypergraph data structure. The resulting model, which we called hypergraph-of-entity, is the main contribution of this thesis. The obtained reduction was achieved by replacing binary edges with n -ary relations based on sets of nodes and entities (undirected document hyperedges), sets of entities (undirected hyperedges, either based on cooccurrence or a grouping by semantic subject), and pairs of a set of terms and a set of one entity (directed hyperedges, mapping text to an object). We introduce the random walk score as the corresponding ranking function, relying on the same idea of seed nodes, similar to the entity weight in the graph-of-entity. Scoring based on this function is highly reliant on the structure of the hypergraph, which we call representation-driven retrieval. As such, we explore several extensions of the hypergraph-of-entity, including relations of synonymy, or contextual similarity, as well as different weighting functions per node and hyperedge type. We also propose TF-bins as a discretization for representing term frequency in the hypergraph-of-entity. For the random walk score, we propose and explore several parameters, including length and repeats, with or without seed node expansion, direction, or weights, and with or without a certain degree of node and/or hyperedge fatigue, a concept that we also propose. For evaluation, we took advantage of TREC 2017 OpenSearch track, which relied on an online evaluation process based on the Living Labs API, and we also participated in TREC 2018 Common Core track, which was based on the newly introduced TREC Washington Post Corpus. Our main experiments were supported on the INEX 2009 Wikipedia collection, which proved to be a fundamental test collection for assessing retrieval effectiveness across multiple tasks. At first, our experiments solely focused on ad hoc document retrieval, ensuring that the model performed adequately for a classical task. We then expanded the work to cover all three entity-oriented search tasks. Results supported the viability of a general retrieval model, opening novel challenges in information retrieval, and proposing a new path towards generality in this area.

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Efficient Estimation of Heat Kernel PageRank for Local Clustering

Cited by 24 publications

References 36 publications

A Review of Graph-Based Models for Entity-Oriented Search

A Review of Graph-Based Models for Entity-Oriented Search

Local clustering via approximate heat kernel PageRank with subgraph sampling

Graph-based entity-oriented search

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