Compact and Scalable Graph Neighborhood Sketching

Akiba, Takuya; Yano, Yosuke

doi:10.1145/2939672.2939762

Cited by 7 publications

(4 citation statements)

References 22 publications

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“…Second, to demonstrate a use case for our models and the selection of length or lifetime variables. Our evaluation here is not meant to assess scalability, as there are several highly scalable implementation of our ingredients: Shortest path searches and distance and reachability sketching [17,24,18,9,4]. We implemented the algorithms in Python and performed the experiments on a Macbook Air and a Linux workstation.…”

Section: Methodsmentioning

confidence: 99%

“…These sketches were originally developed to be used with shortest-paths distances dij and there are several algorithms and large scale implementations that can be used out of the box. They compute the sketches or the more restricted application of neighborhood size estimates [13,9,14,4]. The different algorithms are designed for distributed node-centric, multi-core, and other settings.…”

Section: Algorithms For Reach Kernelsmentioning

confidence: 99%

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Semi-Supervised Learning on Graphs through Reach and Distance Diffusion

Cohen

2016

Preprint

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Semi-supervised learning (SSL) is an indispensable tool when there are few labeled entities and many unlabeled entities for which we want to predict labels. With graph-based methods, entities correspond to nodes in a graph and edges represent strong relations. At the heart of SSL algorithms is the specification of a dense kernel of pairwise affinity values from the graph structure. A learning algorithm is then trained on the kernel together with labeled entities. The most popular kernels are spectral and include the highly scalable "symmetric" Laplacian methods, that compute a soft labels using Jacobi iterations, and "asymmetric" methods including Personalized Page Rank (PPR) which use short random walks and apply with directed relations, such as like, follow, or hyperlinks.We introduce Reach diffusion and Distance diffusion kernels that build on powerful social and economic models of centrality and influence in networks and capture the directed pairwise relations that underline social influence. Inspired by the success of social influence as an alternative to spectral centrality such as Page Rank, we explore SSL with our kernels and develop highly scalable algorithms for parameter setting, label learning, and sampling. We perform preliminary experiments that demonstrate the properties and potential of our kernels.

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Section: Methodsmentioning

confidence: 99%

Section: Algorithms For Reach Kernelsmentioning

confidence: 99%

Semi-Supervised Learning on Graphs through Reach and Distance Diffusion

Cohen

2016

Preprint

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“…Apart from sparsification, other graph summarization techniques such as graph sketching [3,30,44] and graph compression [6,47] have been proposed in recent years. In addition, few graph approximation frameworks have been developed to alleviate performance bottleneck of large graph processing [18,19,46].…”

Section: When Graph Approximation Failsmentioning

confidence: 99%

GraphGuess: Approximate Graph Processing System with Adaptive Correction

Ramezani¹,

Kandemir²,

Sivasubramaniam³

2021

Preprint

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Graph-based data structures have drawn great attention in recent years. The large and rapidly growing trend on developing graph processing systems focus mostly on improving the performance by preprocessing the input graph and modifying its layout. These systems usually take several hours to days to complete processing a single graph on high-end machines, let alone the overhead of preprocessing which most of the time can be dominant. Yet for most of graph applications the exact answer is not always crucial, and providing a rough estimate of the final result is adequate. Approximate computing is introduced to trade off accuracy of results for computation or energy savings that could not be achieved by conventional techniques alone. Although various computing platforms and application domains benefit from approximate computing, it has not been thoroughly explored yet in the context of graph processing systems.In this work, we design, implement and evaluate GraphGuess, inspired from the domain of approximate graph theory and extend it to a general, practical graph processing system. GraphGuess is essentially an approximate graph processing technique with adaptive correction, which can be implemented on top of any graph processing system. We build a vertex-centric processing system based on GraphGuess, where it allows the user to trade off accuracy for better performance. Our experimental studies show that using GraphGuess can significantly reduce the processing time for large scale graphs while maintaining high accuracy.

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“…Although ADS boasts its theoretical guarantee on the time complexity and error bounds, it has difficulties on massive graphs due to its space usage (Akiba and Yano 2016).…”

Section: Introductionmentioning

confidence: 99%

Random-Radius Ball Method for Estimating Closeness Centrality

Inariba

Akiba

Yoshida

2017

AAAI

Self Cite

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In the analysis of real-world complex networks, identifying important vertices is one of the most fundamental operations. A variety of centrality measures have been proposed and extensively studied in various research areas. Many of distance-based centrality measures embrace some issues in treating disconnected networks, which are resolved by the recently emerged harmonic centrality. This paper focuses on a family of centrality measures including the harmonic centrality and its variants, and addresses their computational difficulty on very large graphs by presenting a new estimation algorithm named the random-radius ball (RRB) method. The RRB method is easy to implement, and a theoretical analysis, which includes the time complexity and error bounds, is also provided. The effectiveness of the RRB method over existing algorithms is demonstrated through experiments on real-world networks.

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Compact and Scalable Graph Neighborhood Sketching

Cited by 7 publications

References 22 publications

Semi-Supervised Learning on Graphs through Reach and Distance Diffusion

Semi-Supervised Learning on Graphs through Reach and Distance Diffusion

GraphGuess: Approximate Graph Processing System with Adaptive Correction

Random-Radius Ball Method for Estimating Closeness Centrality

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