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Computing an Aggregate Edge-Weight Function for Clustering Graphs with Multiple Edge Types [article]

Matthew Rocklin, Ali Pinar
2011 arXiv   pre-print
We investigate the community detection problem on graphs in the existence of multiple edge types.  ...  In this paper, we address how to find an aggregation function to generate a composite metric that best resonates with the ground-truth.  ...  Conclusion and Future Work We have discussed the problem of graph clustering with multiple edge types, and studied computing an aggregation function to compute composite edge weights that best resonate  ... 
arXiv:1103.0368v2 fatcat:ijqqrk54fbg43kuqotrrvkkuum

Computing an Aggregate Edge-Weight Function for Clustering Graphs with Multiple Edge Types [chapter]

Matthew Rocklin, Ali Pinar
2010 Lecture Notes in Computer Science  
We investigate the community detection problem on graphs in the existence of multiple edge types.  ...  In this paper, we address how to find an aggregation function to generate a composite metric that best resonates with the ground-truth.  ...  Conclusion and Future Work We have discussed the problem of graph clustering with multiple edge types, and studied computing an aggregation function to compute composite edge weights that best resonate  ... 
doi:10.1007/978-3-642-18009-5_4 fatcat:7rjttfqrkraqxckcatfttkc7xm

On Clustering on Graphs with Multiple Edge Types [article]

Matthew Rocklin, Ali Pinar
2011 arXiv   pre-print
If given the ground-truth clustering, can we recover how the weights for edge types were aggregated?  ...  We study clustering on graphs with multiple edge types. Our main motivation is that similarities between objects can be measured in many different metrics.  ...  with multiple edge types, and a set of clusterings, how do we compute an aggregate weighting function, such that clusters of the aggregate graph will be maximally different that the given set of clusterings  ... 
arXiv:1109.1605v1 fatcat:hhkh7zl4yjdxfguson3zcxl3w4

On Clustering on Graphs with Multiple Edge Types

Matthew Rocklin, Ali Pinar
2013 Internet Mathematics  
If we are given the ground-truth clustering, can we recover how the weights for edge types were aggregated?  ...  We study clustering on graphs with multiple edge types. Our main motivation is that similarities between objects can be measured by many different metrics.  ...  We are also grateful to two anonymous reviewers for their helpful comments on an earlier version of this paper.  ... 
doi:10.1080/15427951.2012.678191 fatcat:durcqj6lcjcddnzoi7vzzwwgga

Large Scale Graph Processing in a Distributed Environment [chapter]

Nitesh Upadhyay, Parita Patel, Unnikrishnan Cheramangalath, Y. N. Srikant
2018 Lecture Notes in Computer Science  
Moreover, some graph algorithms having a high degree of parallelism ideally run on an accelerator cluster.  ...  Existing frameworks do not deal with the accelerator clusters. We propose a framework which addresses the previously stated deficiencies.  ...  Discarding the weight if not required When a weighted graph is given as input to an algorithm that does not use the weight of an edge, storing the weight of edges does not serve any purpose.  ... 
doi:10.1007/978-3-319-75178-8_38 fatcat:gmozi7xbdnfyzerzesccgtdiiq

Latent Clustering on Graphs with Multiple Edge Types [chapter]

Matthew Rocklin, Ali Pinar
2011 Lecture Notes in Computer Science  
We study clustering on graphs with multiple edge types.  ...  We generalize the concept of clustering in single-edge graphs to multi-edged graphs and discuss how this generates a space of clusterings.  ...  Ongoing work includes more intelligent sampling (intentionally finding distinct clusterings), effects of adding non-linear combinations of edge-types, and searching the space for clusterings with desired  ... 
doi:10.1007/978-3-642-21286-4_4 fatcat:n22twfsjvbewhetqqsbzjiecwa

Locally Boosted Graph Aggregation for Community Detection [article]

Jeremy Kun, Rajmonda Caceres, Kevin Carter
2014 arXiv   pre-print
Building on previous work, we explore the extent to which different local quality measurements yield graph representations that are suitable for community detection.  ...  Finally, we prove a convergence theorem in an ideal setting and outline future research into other application domains.  ...  Acknowledgements We thank Vineet Mehta for providing us with the Enron topic model data, and for his many helpful discussions.  ... 
arXiv:1405.3210v1 fatcat:ixdj2y7i5zeuboy2csqszrr3ca

ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations

Ekagra Ranjan, Soumya Sanyal, Partha Talukdar
2020 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
It also learns a sparse soft cluster assignment for nodes at each layer to effectively pool the subgraphs to form the pooled graph.  ...  Our experimental results show that combining existing GNN architectures with ASAP leads to state-of-the-art results on multiple graph classification benchmarks.  ...  Effect of computing Soft edge weights We evaluate the importance of calculating edge weights for the pooled graph as defined in Eq. 10.  ... 
doi:10.1609/aaai.v34i04.5997 fatcat:fgvneimypfevfjyc3etptm422q

A Boosting Approach to Learning Graph Representations [article]

Rajmonda Caceres, Kevin Carter, Jeremy Kun
2014 arXiv   pre-print
Our framework leads to suitable global graph representations from quality measurements local to each edge.  ...  We explore the extent to which different quality measurements yield graph representations that are suitable for community detection.  ...  [25] discuss transformations to heterogeneous graphs (graphs with multiple node types and/or multiple edge types) in order to improve the quality of a learning algorithm such as community detection  ... 
arXiv:1401.3258v1 fatcat:y2lpysz5obfclhgprccejkyiqq

GiViP: A Visual Profiler for Distributed Graph Processing Systems [article]

Alessio Arleo, Walter Didimo, Giuseppe Liotta, Fabrizio Montecchiani
2017 arXiv   pre-print
GiViP captures the huge amount of messages exchanged throughout a computation and provides an interactive user interface for the visual analysis of the collected data.  ...  While many graph processing systems working on top of distributed infrastructures have been proposed to deal with big graphs, the tasks of profiling and debugging their massive computations remain time  ...  with (dynamic) edge weights.  ... 
arXiv:1708.07985v2 fatcat:zmetbkdrczfdzkqo6vknvfflgy

GiViP: A Visual Profiler for Distributed Graph Processing Systems [chapter]

Alessio Arleo, Walter Didimo, Giuseppe Liotta, Fabrizio Montecchiani
2018 Lecture Notes in Computer Science  
GiViP captures the huge amount of messages exchanged throughout a computation and provides an interactive user interface for the visual analysis of the collected data.  ...  While many graph processing systems working on top of distributed infrastructures have been proposed to deal with big graphs, the tasks of profiling and debugging their massive computations remain time  ...  with (dynamic) edge weights.  ... 
doi:10.1007/978-3-319-73915-1_21 fatcat:qoe32n2rejc73mngyxjovkvidi

On Summarizing Graph Streams [article]

Nan Tang, Qing Chen, Prasenjit Mitra
2015 arXiv   pre-print
Specifically, given a graph stream G, directed or undirected, the objective is to summarize G as S with much smaller (sublinear) space, linear construction time and constant maintenance cost for each edge  ...  We present gLava, a probabilistic graph model that, instead of treating an edge (a stream element) as the operating unit, uses the finer grained node in an element.  ...  We have proposed a new graph sketch for summarizing graph streams. We have demonstrated its wide applicability to many emerging applications.  ... 
arXiv:1510.02219v1 fatcat:rcszktypbbgl3gdw6n55silmyi

One trillion edges

Avery Ching, Sergey Edunov, Maja Kabiljo, Dionysios Logothetis, Sambavi Muthukrishnan
2015 Proceedings of the VLDB Endowment  
Analyzing large graphs provides valuable insights for social networking and web companies in content ranking and recommendations.  ...  In this paper, we describe the usability, performance, and scalability improvements we made to Apache Giraph, an open-source graph processing system, in order to use it on Facebook-scale graphs of up to  ...  Giraph has greatly extended the basic Pregel model with new functionality such as master computation, sharded aggregators, edge-oriented input, out-of-core computation, composable computation, and more  ... 
doi:10.14778/2824032.2824077 fatcat:wfivemamp5grpjc5mwqgrmfd5u

ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations [article]

Ekagra Ranjan, Soumya Sanyal, Partha Pratim Talukdar
2020 arXiv   pre-print
It also learns a sparse soft cluster assignment for nodes at each layer to effectively pool the subgraphs to form the pooled graph.  ...  Our experimental results show that combining existing GNN architectures with ASAP leads to state-of-the-art results on multiple graph classification benchmarks.  ...  We would like to thank Matthias Fey again for actively maintaining the library and quickly responding to our queries on github.  ... 
arXiv:1911.07979v3 fatcat:5zrtrymwxrdkljwq72yimo7ijm

Fast Iterative Graph Computation with Resource Aware Graph Parallel Abstractions

Yang Zhou, Ling Liu, Kisung Lee, Calton Pu, Qi Zhang
2015 Proceedings of the 24th International Symposium on High-Performance Parallel and Distributed Computing - HPDC '15  
GraphLego is novel in three aspects: (1) we argue that vertex-centric or edge-centric graph partitioning are ineffective for parallel processing of large graphs and we introduce three alternative graph  ...  Iterative computation on large graphs has challenged system research from two aspects: (1) how to conduct high performance parallel processing for both in-memory and outof-core graphs; and (2) how to handle  ...  This material is based upon work partially supported by the National Science Foundation under Grants IIS-0905493, CNS-1115375, IIP-1230740, and a grant from Intel ISTC on Cloud Computing.  ... 
doi:10.1145/2749246.2749258 dblp:conf/hpdc/ZhouLLPZ15 fatcat:phznimatljchnghh4sjxj5t74m
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