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Computing an Aggregate Edge-Weight Function for Clustering Graphs with Multiple Edge Types
[article]
2011
arXiv
pre-print
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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]
2010
Lecture Notes in Computer Science
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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]
2011
arXiv
pre-print
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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
2013
Internet Mathematics
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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]
2018
Lecture Notes in Computer Science
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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]
2011
Lecture Notes in Computer Science
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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]
2014
arXiv
pre-print
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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
2020
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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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]
2014
arXiv
pre-print
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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]
2017
arXiv
pre-print
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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]
2018
Lecture Notes in Computer Science
Preserved Fulltext
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The file type is application/pdf.
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]
2015
arXiv
pre-print
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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
2015
Proceedings of the VLDB Endowment
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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]
2020
arXiv
pre-print
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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
2015
Proceedings of the 24th International Symposium on High-Performance Parallel and Distributed Computing - HPDC '15
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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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