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Efficient pagerank approximation via graph aggregation

Andrei Z. Broder, Ronny Lempel, Farzin Maghoul, Jan Pedersen
2004 Proceedings of the 13th international World Wide Web conference on Alternate track papers & posters - WWW Alt. '04  
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

Andrei Z. Broder, Ronny Lempel, Farzin Maghoul, Jan Pedersen
2004 Alternate track papers & posters of the 13th international conference on World Wide Web - WWW Alt. '04  
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

A. Z. Broder, R. Lempel, F. Maghoul, J. Pedersen
2006 Information retrieval (Boston)  
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

Predict then Propagate: Graph Neural Networks meet Personalized PageRank [article]

Johannes Gasteiger, Aleksandar Bojchevski, Stephan Günnemann
2022 arXiv   pre-print
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

A Local Updating Algorithm for Personalized PageRank via Chebyshev Polynomials [article]

Esteban Bautista, Matthieu Latapy
2021 arXiv   pre-print
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

Personalized PageRank Graph Attention Networks [article]

Julie Choi
2022 arXiv   pre-print
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

Anand Padmanabha Iyer, Li Erran Li, Tathagata Das, Ion Stoica
2016 Proceedings of the Fourth International Workshop on Graph Data Management Experiences and Systems - GRADES '16  
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

Nan Li, Ziyu Guan, Lijie Ren, Jian Wu, Jiawei Han, Xifeng Yan
2013 2013 IEEE 29th International Conference on Data Engineering (ICDE)  
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

Hideaki Ishii, Roberto Tempo, Er-Wei Bai
2012 IEEE Transactions on Automatic Control  
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

Yao Wu, Louiqa Raschid
2009 Proceedings / International Conference on Data Engineering  
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]

Christian Kohlschütter, Paul-Alexandru Chirita, Wolfgang Nejdl
2006 Lecture Notes in Computer Science  
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

A yoke of oxen and a thousand chickens for heavy lifting graph processing

Abdullah Gharaibeh, Lauro Beltrão Costa, Elizeu Santos-Neto, Matei Ripeanu
2012 Proceedings of the 21st international conference on Parallel architectures and compilation techniques - PACT '12  
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

David Simmen, Karl Schnaitter, Jeff Davis, Yingjie He, Sangeet Lohariwala, Ajay Mysore, Vinayak Shenoi, Mingfeng Tan, Yu Xiao
2014 Proceedings of the VLDB Endowment  
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

REX: Recursive, Delta-Based Data-Centric Computation [article]

Svilen R. Mihaylov, Zachary G. Ives, Sudipto Guha
2012 arXiv   pre-print
DBMSs that support recursive SQL are more efficient in that they propagate only the changes in each step -- but they still accumulate each iteration's state, even if it is no longer useful.  ...  to unify the strengths of both styles of platforms, with a focus on supporting iterative computations in which changes, in the form of deltas, are propagated from iteration to iteration, and state is efficiently  ...  Consider a directed graph stored as an edge relation, partitioned across multiple machines by vertexId. We want to compute the PageRank value for each vertex in the graph.  ... 
arXiv:1208.0089v1 fatcat:kc55jzf3e5fqlme5xxwkrqplu4

Reduce and aggregate

Alessandro Epasto, Jon Feldman, Silvio Lattanzi, Stefano Leonardi, Vahab Mirrokni
2014 Proceedings of the 23rd international conference on World wide web - WWW '14  
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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