Approximate Personalized PageRank on Dynamic Graphs

Zhang, Hongyang; Lofgren, Peter; Goel, Ashish

doi:10.1145/2939672.2939804

Cited by 69 publications

(71 citation statements)

References 23 publications

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“…Recall that PRSim generates the index by performing the backward search algorithm. It is shown in [44] that the results of the backward search to a randomly selected target node w can be maintained with cost O(k +d ε ), where k is the total number of insertions/deletions. Since our index stores the results of the backward search for j 0 target nodes, it can process k insertions/deletions in O(kj 0 + m ε ) time.…”

Section: Algorithm 4: Query Algorithmmentioning

confidence: 99%

PRSim

Wei

He²,

Xiao

et al. 2019

Proceedings of the 2019 International Conference on Management of Data

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SimRank is a classic measure of the similarities of nodes in a graph. Given a node u in graph G = (V , E), a single-source SimRank query returns the SimRank similarities s(u, ) between node u and each node ∈ V . This type of queries has numerous applications in web search and social networks analysis, such as link prediction, web mining, and spam detection. Existing methods for single-source SimRank queries, however, incur query cost at least linear to the number of nodes n, which renders them inapplicable for real-time and interactive analysis. This paper proposes PRSim, an algorithm that exploits the structure of graphs to efficiently answer single-source SimRank queries. PRSim uses an index of size O(m), where m is the number of edges in the graph, and guarantees a query time that depends on the reverse PageRank distribution of the input graph. In particular, we prove that PRSim runs in sub-linear time if the degree distribution of the input * Work partly done at Beijing Key Laboratory of Big Data Management and Analysis Method, Renmin University of China. † Ji-Rong Wen is the corresponding author.Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org.

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Section: Algorithm 4: Query Algorithmmentioning

confidence: 99%

PRSim

Wei

He²,

Xiao

et al. 2019

Proceedings of the 2019 International Conference on Management of Data

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“…Finally, it is worth mentioning that existing work has studied other variants of PPR queries, such as point-to-point PPR queries [16,[29][30][31]39], PPR queries on dynamic graphs [12,13,33,35,43,44], distributed algorithms for PPR [11,21]. These studies, however, are orthogonal to our work.…”

Section: Other Related Workmentioning

confidence: 99%

TopPPR

Wei

Xiao

et al. 2018

Proceedings of the 2018 International Conference on Management of Data

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“…anks to improved data collection and sensing technology, such data are of growing importance in various se ings. Important applications include, e.g., cluster detection in temporal graphs capturing economic transactions or social interactions [15,21], ranking nodes in dynamic social networks [25,38], or identifying frequent interaction pa erns in communication networks [37]. Despite their importance, the analysis of such data is still a considerable challenge.…”

Section: Related Workmentioning

confidence: 99%

When is a Network a Network?

Scholtes

2017

Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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We introduce a framework for the modeling of sequential data capturing pathways of varying lengths observed in a network. Such data are important, e.g., when studying click streams in information networks, travel pa erns in transportation systems, information cascades in social networks, biological pathways or time-stamped social interactions. While it is common to apply graph analytics and network analysis to such data, recent works have shown that temporal correlations can invalidate the results of such methods. is raises a fundamental question: when is a network abstraction of sequential data justi ed? Addressing this open question, we propose a framework which combines Markov chains of multiple, higher orders into a multi-layer graphical model that captures temporal correlations in pathways at multiple length scales simultaneously. We develop a model selection technique to infer the optimal number of layers of such a model and show that it outperforms previously used Markov order detection techniques. An application to eight real-world data sets on pathways and temporal networks shows that it allows to infer graphical models which capture both topological and temporal characteristics of such data. Our work highlights fallacies of network abstractions and provides a principled answer to the open question when they are justi ed. Generalizing network representations to multi-order graphical models, it opens perspectives for new data mining and knowledge discovery algorithms.

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Approximate Personalized PageRank on Dynamic Graphs

Cited by 69 publications

References 23 publications

PRSim

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When is a Network a Network?

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