Approximate Personalized PageRank on Dynamic Graphs

Zhang, Hongyang; Lofgren, Peter; Goel, Ashish

doi:10.48550/arxiv.1603.07796

Cited by 4 publications

(14 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In Theorem 3.2, we show that the sum of r old and r offset becomes the exact RWR score vector of the updated graph G + ∆G. Our result is the first exactness guarantee for propagation approaches [16,26] on dynamic graphs. Theorem 3.2 (Exactness of OSP).…”

Section: Osp: Offset Score Propagationmentioning

confidence: 70%

“…For RWR computation, q CPI = cq is set with an initial score c at seed index s. Unlike other propagation methods such as Gauss-Southwell algorithm [16] and Forward Local Push algorithm [26], CPI computes RWR with accuracy assurance and general time complexity model. Thus we propose dynamic RWR method OSP and OSP-T using CPI to provide theoretical guarantees on the error and the convergence.…”

Section: Cpi: Cumulative Power Iterationmentioning

confidence: 99%

“…Bahmani et al [4] applied the Monte-Carlo method [10] on the dynamic RWR problem. Recently, score propagation models were proposed by Ohsaka et al [16] and Zhang et al [26]; Ohsaka et al proposed TrackingPPR which propagates scores using Gauss-Southwell algorithm; Zhang et al proposed LazyForward which optimizes the initial step from TrackingPPR and propagates scores using Forward Push algorithm. They succeed in outperforming the previous approaches in both running time and accuracy [16,26].…”

Section: Introductionmentioning

confidence: 99%

“…Then OSP merges the result of the propagation with the current RWR scores to get an updated RWR scores. Unlike the previous propagation models [16,26], OSP gives exact updated RWR scores. We also propose OSP-T, a version of OSP, with provable error bound and running time: given restart probability c and error tolerance ϵ, OSP-T computes RWR scores with O(ϵ/c) error in O(log (1−c) ( ϵ 2 )) iterations in dynamic graphs.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Fast and Accurate Random Walk with Restart on Dynamic Graphs with Guarantees

Yoon

Jin

Kang

2018

Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18

View full text Add to dashboard Cite

Given a time-evolving graph, how can we track similarity between nodes in a fast and accurate way, with theoretical guarantees on the convergence and the error? Random Walk with Restart (RWR) is a popular measure to estimate the similarity between nodes and has been exploited in numerous applications. Many real-world graphs are dynamic with frequent insertion/deletion of edges; thus, tracking RWR scores on dynamic graphs in an efficient way has aroused much interest among data mining researchers. Recently, dynamic RWR models based on the propagation of scores across a given graph have been proposed, and have succeeded in outperforming previous other approaches to compute RWR dynamically. However, those models fail to guarantee exactness and convergence time for updating RWR in a generalized form.In this paper, we propose OSP, a fast and accurate algorithm for computing dynamic RWR with insertion/deletion of nodes/edges in a directed/undirected graph. When the graph is updated, OSP first calculates offset scores around the modified edges, propagates the offset scores across the updated graph, and then merges them with the current RWR scores to get updated RWR scores. We prove the exactness of OSP and introduce OSP-T, a version of OSP which regulates a trade-off between accuracy and computation time by using error tolerance ϵ. Given restart probability c, OSP-T guarantees to return RWR scores with O(ϵ/c) error in O(log (1−c) ( ϵ 2 )) iterations. Through extensive experiments, we show that OSP tracks RWR exactly up to 4605× faster than existing static RWR method on dynamic graphs, and OSP-T requires up to 15× less time with 730× lower L1 norm error and 3.3× lower rank error than other state-of-the-art dynamic RWR methods.

show abstract

Section: Osp: Offset Score Propagationmentioning

confidence: 70%

Section: Cpi: Cumulative Power Iterationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fast and Accurate Random Walk with Restart on Dynamic Graphs with Guarantees

Yoon

Jin

Kang

2018

Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18

View full text Add to dashboard Cite

show abstract

“…Proof. We first make an assumption that there is only one edge update (𝑢, 𝑣, event) in ΔG 𝑡 , then based Lemma 14 of [46], the run time of per-edge update is at most:…”

Section: Dynamic Graph Embedding For Single Batchmentioning

confidence: 99%

Subset Node Representation Learning over Large Dynamic Graphs

Guo¹,

Zhou²,

Skiena³

2021

Preprint

View full text Add to dashboard Cite

Dynamic graph representation learning is a task to learn node embeddings over dynamic networks, and has many important applications, including knowledge graphs, citation networks to social networks. Graphs of this type are usually large-scale but only a small subset of vertices are related in downstream tasks. Current methods are too expensive to this setting as the complexity is at best linear-dependent on both the number of nodes and edges.In this paper, we propose a new method, namely Dynamic Personalized PageRank Embedding (DynamicPPE) for learning a target subset of node representations over large-scale dynamic networks. Based on recent advances in local node embedding and a novel computation of dynamic personalized PageRank vector (PPV), Dy-namicPPE has two key ingredients: 1) the per-PPV complexity is O (𝑚 d/𝜖) where 𝑚, d, and 𝜖 are the number of edges received, average degree, global precision error respectively. Thus, the per-edge event update of a single node is only dependent on d in average; and 2) by using these high quality PPVs and hash kernels, the learned embeddings have properties of both locality and global consistency. These two make it possible to capture the evolution of graph structure effectively.Experimental results demonstrate both the effectiveness and efficiency of the proposed method over large-scale dynamic networks. We apply DynamicPPE to capture the embedding change of Chinese cities in the Wikipedia graph during this ongoing COVID-19 pandemic 1 . Our results show that these representations successfully encode the dynamics of the Wikipedia graph.

show abstract

Accelerating dynamic graph analytics on GPUs

Sha

et al. 2017

Proc. VLDB Endow.

View full text Add to dashboard Cite

As graph analytics often involves compute-intensive operations, GPUs have been extensively used to accelerate the processing. However, in many applications such as social networks, cyber security, and fraud detection, their representative graphs evolve frequently and one has to perform a rebuild of the graph structure on GPUs to incorporate the updates. Hence, rebuilding the graphs becomes the bottleneck of processing high-speed graph streams. In this paper, we propose a GPU-based dynamic graph storage scheme to support existing graph algorithms easily. Furthermore, we propose parallel update algorithms to support efficient stream updates so that the maintained graph is immediately available for high-speed analytic processing on GPUs. Our extensive experiments with three streaming applications on large-scale real and synthetic datasets demonstrate the superior performance of our proposed approach.

show abstract

Approximate Personalized PageRank on Dynamic Graphs

Cited by 4 publications

References 13 publications

Fast and Accurate Random Walk with Restart on Dynamic Graphs with Guarantees

Fast and Accurate Random Walk with Restart on Dynamic Graphs with Guarantees

Subset Node Representation Learning over Large Dynamic Graphs

Accelerating dynamic graph analytics on GPUs

Contact Info

Product

Resources

About