Index design and query processing for graph conductance search

RWR (Random Walk with Restart) is frequently used by many graph-based ranking algorithms, but it does not consider a signed graph where edges may have negative weight values. In this paper, we apply the Balance Theory by F. Heider to RWR over a signed graph and propose a novel RWR, Balanced Random Walk (BRW). We apply the proposed technique into the domain of recommendation system, and show by experiments its effectiveness to filter out the items that users may dislike. In order to provide the reasonable performance of BRW in the domain, we modify the existing Top-k algorithm, BCA, and propose a new algorithm, Bicolor-BCA. The proposed algorithm yet requires employing a threshold. In the experiment, we show how threshold values affect both precision and performance of the algorithm.

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“…As the RWR model calculates the probability that a random user will stay at a node at a certain time, [5]. And the last category…”

Section: Algorithms In the First Category Exploitmentioning

confidence: 99%

Fast Random Walk with Restart over a Signed Graph

Myung

Shim

Suh

2015

The Journal of Society for e-Business Studies

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“…Second, only sampled paths using a Monte Carlo simulation are considered, e.g., [8,2]. Third, only neighborhood paths that are within some "radius" around q are considered, e.g., Bookmark Coloring [5] and the HubRank family [6,7,16].…”

Section: Related Workmentioning

confidence: 99%

“…Inspired by inverse P-distance [12] and the ideas of hubs and subgraph [5,7], we introduce a novel principle of scheduled approximation by partitioning and prioritizing computation. This principle leads to our incremental query processing by iteratively enhancing the estimated PPV, which is also accuracy-aware-we can measure some form of error in our estimation after each iteration at query time.…”

Section: Related Workmentioning

confidence: 99%

“…While such partial personalization is in some cases acceptable, most applications demand full personalization, which supports any arbitrary node as queries. Thus, some recent efforts [12,8,5,6,7] propose full personalization methods for approximate PPVs. They trade accuracy for faster query processing by reducing the computation in consideration online, as well as resorting to partial precomputation offline, which we will further elaborate in Sect.…”

Section: Introductionmentioning

confidence: 99%

“…Finally, we conduct extensive experiments on two real-world datasets. We compare FastPPV with two competitive baselines [7,8], and find out that FastPPV significantly outperforms them in both online and offline phases. More importantly, we are able to demonstrate the scalability of FastPPV on growing graphs.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Incremental and accuracy-aware personalized pagerank through scheduled approximation

2013

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As Personalized PageRank has been widely leveraged for ranking on a graph, the efficient computation of Personalized PageRank Vector (PPV) becomes a prominent issue. In this paper, we propose FastPPV, an approximate PPV computation algorithm that is incremental and accuracy-aware. Our approach hinges on a novel paradigm of scheduled approximation: the computation is partitioned and scheduled for processing in an "organized" way, such that we can gradually improve our PPV estimation in an incremental manner, and quantify the accuracy of our approximation at query time. Guided by this principle, we develop an efficient hub based realization, where we adopt the metric of hub-length to partition and schedule random walk tours so that the approximation error reduces exponentially over iterations. Furthermore, as tours are segmented by hubs, the shared substructures between different tours (around the same hub) can be reused to speed up query processing both within and across iterations. Finally, we evaluate FastPPV over two real-world graphs, and show that it not only significantly outperforms two state-of-the-art baselines in both online and offline phrases, but also scale well on larger graphs. In particular, we are able to achieve near-constant time online query processing irrespective of graph size.

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Ranking-Based Community Detection for Social Networks

Pirouz

2021

Proceedings of the Future Technologies Conference (FTC) 2021, Volume 3

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Index design and query processing for graph conductance search

Cited by 32 publications

References 40 publications

Fast Random Walk with Restart over a Signed Graph

Fast Random Walk with Restart over a Signed Graph

Incremental and accuracy-aware personalized pagerank through scheduled approximation

Ranking-Based Community Detection for Social Networks

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