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Efficient pagerank approximation via graph aggregation
2004
Proceedings of the 13th international World Wide Web conference on Alternate track papers & posters - WWW Alt. '04
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We present a framework for approximating random-walk based probability distributions over Web pages using graph aggregation. ...
In particular, our framework can approximate the well-known PageRank distribution by setting the classes according to the set of pages on each Web host. ...
Experiments The section reports on experiments with a specific flavor of host-aggregated PageRank approximation. ...
doi:10.1145/1013367.1013537
dblp:conf/www/BroderLMP04
fatcat:srdwtq65g5enblxefzllvctasy
Efficient pagerank approximation via graph aggregation
2004
Alternate track papers & posters of the 13th international conference on World Wide Web - WWW Alt. '04
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We present a framework for approximating random-walk based probability distributions over Web pages using graph aggregation. ...
In particular, our framework can approximate the well-known PageRank distribution by setting the classes according to the set of pages on each Web host. ...
Experiments The section reports on experiments with a specific flavor of host-aggregated PageRank approximation. ...
doi:10.1145/1010432.1010602
fatcat:teaq7qbmqnh53do5pueggahhuu
Efficient PageRank approximation via graph aggregation
2006
Information retrieval (Boston)
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We present a framework for approximating random-walk based probability distributions over Web pages using graph aggregation. ...
In particular, our framework can approximate the well-known PageRank distribution by setting the classes according to the set of pages on each Web host. ...
Experiments The section reports on experiments with a specific flavor of host-aggregated PageRank approximation. ...
doi:10.1007/s10791-006-7146-1
fatcat:uf4fxsem5ndxvcvn2nr2wqgiem
A Local Updating Algorithm for Personalized PageRank via Chebyshev Polynomials
[article]
2021
arXiv
pre-print
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To address this limitation, this work proposes a novel distributed algorithm to locally update personalized PageRank vectors when the graph topology changes. ...
generalizations of PageRank for which no updating algorithms have been developed. ...
For this experiment, we use the aggregated first 100 snapshots from the Tech-AS-Topology network as initial graph. ...
arXiv:2110.02538v1
fatcat:2rkseaasovbsznopbhoem6p2ou
Predict then Propagate: Graph Neural Networks meet Personalized PageRank
[article]
2022
arXiv
pre-print
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2019 pre-print arXiv:1810.05997v5 fatcat:5seo3sghzva5rf3py455i6jehmOther Versions
In this paper, we use the relationship between graph convolutional networks (GCN) and PageRank to derive an improved propagation scheme based on personalized PageRank. ...
We utilize this propagation procedure to construct a simple model, personalized propagation of neural predictions (PPNP), and its fast approximation, APPNP. ...
Hence, we can approximate PPNP via an approximate computation of topic-sensitive PageRank. Approximate personalized propagation of neural predictions (APPNP). ...
arXiv:1810.05997v6
fatcat:cyirdkbwgzcpjjphn26wtmd76i
Personalized PageRank Graph Attention Networks
[article]
2022
arXiv
pre-print
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GNNs provide a general and efficient framework to learn from graph-structured data. ...
Intuitively, message aggregation based on Personalized PageRank corresponds to infinitely many neighborhood aggregation layers. ...
We are interested in efficient and scalable algorithms for computing (an approximation of) PPR. Random walk sampling [13] is one such approximation technique. ...
arXiv:2205.14259v1
fatcat:xyccmktkobf4rdvtipxpsbog6m
Time-evolving graph processing at scale
2016
Proceedings of the Fourth International Workshop on Graph Data Management Experiences and Systems - GRADES '16
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However, existing graph processing systems lack support for efficient computations on dynamic graphs. ...
G T quickly builds faulttolerant graph snapshots as each small batch of new data arrives. G T achieves high performance and fault tolerant graph stream processing via a number of optimizations. ...
If the aggregation functions are also invertible, a more efficient version also takes a function for "subtracting" graphs and maintains the state incrementally. ...
doi:10.1145/2960414.2960419
dblp:conf/grades/IyerLDS16
fatcat:tks4gkhimzhtzocriqu3vxwrle
gIceberg: Towards iceberg analysis in large graphs
2013
2013 IEEE 29th International Conference on Data Engineering (ICDE)
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In this paper, we introduce the concept of graph icebergs that refer to vertices for which the concentration (aggregation) of an attribute in their vicinities is abnormally high. ...
To improve scalability, two aggregation strategies, forward and backward aggregation, are proposed with corresponding optimization techniques and bounds. ...
interesting to a certain query via aggregation. ...
doi:10.1109/icde.2013.6544894
dblp:conf/icde/LiGRWHY13
fatcat:eysgsnmwxfbsfhcdjqmufmxmrq
A Web Aggregation Approach for Distributed Randomized PageRank Algorithms
2012
IEEE Transactions on Automatic Control
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2012 pre-print arXiv:1203.6606v1 fatcat:a4wrrj4iszfi7i6e3ndx7brtq4
For each group, an aggregated PageRank value is computed, which can then be distributed among the group members. ...
We provide a distributed update scheme for the aggregated PageRank along with an analysis on its convergence properties. ...
Aggregation-based PageRank computation In this section, we present the approach for aggregating the web graph and then propose an approximated version of the PageRank that can be computed from a lower-order ...
doi:10.1109/tac.2012.2190161
fatcat:5hkqlowa5zajvi2avne7du2qby
ApproxRank: Estimating Rank for a Subgraph
2009
Proceedings / International Conference on Data Engineering
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The challenge for these applications is to compute PageRank-style scores efficiently on the subgraph, i.e., the ranking must reflect the global link structure of the Web graph but it must do so without ...
We demonstrate that ApproxRank provides a good approximation to PageRank for a variety of subgraphs. ...
We propose a framework of an exact solution and an approximate solution for computing PageRank on a subgraph. ...
doi:10.1109/icde.2009.108
dblp:conf/icde/WuR09
fatcat:32sgr4ph7fcrfmgxzlzdijyoya
Efficient Parallel Computation of PageRank
[chapter]
2006
Lecture Notes in Computer Science
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By introducing a two-dimensional web model and by adapting the PageRank to this environment, we present and evaluate efficient methods to compute the exact rank vector even for large-scale web graphs in ...
PageRank inherently is massively parallelizable and distributable, as a result of web's strict host-based link locality. ...
Conclusions and Further Work We presented an efficient method to perform the PageRank calculation in parallel over arbitrary large web graphs. ...
doi:10.1007/11735106_22
fatcat:cwwh7j2775e4tgunvoob5iyqqa
Approximating Personalized PageRank with Minimal Use of Web Graph Data
2006
Internet Mathematics
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In this paper, we consider the problem of calculating fast and accurate approximations to the personalized PageRank score of a webpage. ...
We report experiments with these algorithms on web graphs of up to 118 million pages and prove a theoretical approximation bound for all. ...
In this paper, we present accurate and efficient algorithms for computing approximations of personalized PageRank without having to access the entire web graph matrix. ...
doi:10.1080/15427951.2006.10129128
fatcat:upj2qb3z35cp7gqgw74ndjyxny
A yoke of oxen and a thousand chickens for heavy lifting graph processing
2012
Proceedings of the 21st international conference on Parallel architectures and compilation techniques - PACT '12
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algorithms on heterogeneous platforms; and, (iii) demonstrates TOTEM'S efficiency by implementing and evaluating two graph algorithms (PageRank and breadth-first search). ...
Large, real-world graphs are famously difficult to process efficiently. ...
This is efficient for algorithms that communicate via each edge in every superstep, such as PageRank. ...
doi:10.1145/2370816.2370866
dblp:conf/IEEEpact/GharaibehCSR12
fatcat:oiym75qbwzgvfcembm4u76t4j4
Large-scale graph analytics in Aster 6
2014
Proceedings of the VLDB Endowment
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Graph analytics is an important big data discovery technique. ...
Specialized platforms have emerged to satisfy the unique processing requirements of large-scale graph analytics; however, these platforms do not enable graph analytics to be combined with other analytics ...
The method can also make initial updates to aggregators. Aggregators are available via the GraphGlobals object. ...
doi:10.14778/2733004.2733013
fatcat:cxuw36jmcre2ppzoz5tiotqp3u
Reduce and aggregate
2014
Proceedings of the 23rd international conference on World wide web - WWW '14
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We show how to tackle the imbalance in the graphs to speed up the computation and provide efficient real-time algorithms for computing rankings for an arbitrary subset of categories. ...
We present a novel algorithmic framework that addresses both issues for the computation of several graph-theoretical similarity measures, including # common neighbors, and Personalized PageRank. ...
The operator aggregate computes a ranking of actor set A by similarity to a ∈ A in the graph A ∪ C1 ∪ . . . ∪ Cc; this is achieved via fast aggregation of the information stored in the reduced graph of ...
doi:10.1145/2566486.2568025
dblp:conf/www/EpastoFLLM14
fatcat:g4tmyynkufd55f72baddq4nirq
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