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On Clustering on Graphs with Multiple Edge Types
[article]
2011
arXiv
pre-print
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We study clustering on graphs with multiple edge types. Our main motivation is that similarities between objects can be measured in many different metrics. ...
As such, graphs with multiple edges is a more accurate model to describe similarities between objects. ...
This paper studies the community detection problem on graphs with multiple edge types or multiple similarity metrics, as opposed to traditional graphs with a single edge type. ...
arXiv:1109.1605v1
fatcat:hhkh7zl4yjdxfguson3zcxl3w4
On Clustering on Graphs with Multiple Edge Types
2013
Internet Mathematics
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We study clustering on graphs with multiple edge types. Our main motivation is that similarities between objects can be measured by many different metrics. ...
As such, graphs with multiple edges give a more accurate model to describe similarities between objects than models using single-edge graphs. ...
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
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
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. ...
We describe two approaches: solving an inverse problem where we try to find parameters that generate a graph whose clustering gives the ground-truth clustering, and choosing parameters to maximize the ...
This paper studies the community detection problem on networks with multiple edges types or multiple similarity metrics, as opposed to traditional networks with a single edge type. ...
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. ...
We describe two approaches: solving an inverse problem where we try to find parameters that generate a graph whose clustering gives the ground-truth clustering, and choosing parameters to maximize the ...
This paper studies the community detection problem on networks with multiple edges types or multiple similarity metrics, as opposed to traditional networks with a single edge type. ...
doi:10.1007/978-3-642-18009-5_4
fatcat:7rjttfqrkraqxckcatfttkc7xm
EdgeCentric: Anomaly Detection in Edge-Attributed Networks
[article]
2015
arXiv
pre-print
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Given a network with attributed edges, how can we identify anomalous behavior? Networks with edge attributes are commonplace in the real world. ...
Our work has a number of notable contributions, including (a) formulation: while most other graph-based anomaly detection works use structural graph connectivity or node information, we focus on the new ...
Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on. ...
arXiv:1510.05544v2
fatcat:mmqhugfqnvewzdyppmfabradn4
Integrating Vertex-centric Clustering with Edge-centric Clustering for Meta Path Graph Analysis
2015
Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '15
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We perform clustering analysis on both a unified vertex-centric path graph and each edge-centric path graph to generate vertex clustering and edge clusterings of the original heterogeneous network respectively ...
We model a heterogeneous network containing M types of meta paths as M vertex-centric path graphs and M edge-centric path graphs. ...
multiple types of meta paths in terms of multiple vertex-centric path graphs and multiple edge-centric path graphs. ...
doi:10.1145/2783258.2783328
dblp:conf/kdd/ZhouLB15
fatcat:6zj4erfqnfd6dfxhoqhtlwqxii
Clustering Attributed Multi-graphs with Information Ranking
[chapter]
2015
Lecture Notes in Computer Science
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Attributed multi-graphs are data structures to model realworld networks of objects which have rich properties/attributes and they are connected by multiple types of edges. ...
In this paper, we propose an efficient method for Clustering Attributed Multi-graphs with Information Ranking, namely CAMIR. ...
HASCOP [2] is one approach that considers the different importance of vertex attributes and multiple edge-types. ...
doi:10.1007/978-3-319-22849-5_29
fatcat:idq3pbksezdxbhaudl74pltrca
Computing Quartet Distance is Equivalent to Counting 4-Cycles
[article]
2020
arXiv
pre-print
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2018 pre-print arXiv:1811.06244v1 fatcat:422pn6ibsvhwrj5z6ge7uef6uy
of counting 4-cycles in an undirected simple graph with m edges. ...
For trees with unbounded degrees we obtain an 𝒪(n^1.48) time algorithm, which is a substantial improvement on the previous bound of 𝒪(n^2log n). ...
We say that an edges is of type (a)-(b) for a, b ∈ {1, 2, 3}, when it connects a node of type (a) on one side of the graph and (b) on the other. ...
arXiv:1811.06244v2
fatcat:kdd6gz5g6zcfrb6wnjx2ztgpim
Fragmenta: A theory of fragmentation for MDE
2015
2015 ACM/IEEE 18th International Conference on Model Driven Engineering Languages and Systems (MODELS)
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The theory is based on an algebraic description of models, fragments and clusters based on graphs and morphisms. ...
Typed Models with Fragmentation Strategies Model typing builds up on fragment typing and FSs enrich model typing. ...
We call association edges to edges of type composition, relation and link. All relation and composition edges (and no other) have multiplicities. ...
doi:10.1109/models.2015.7338241
dblp:conf/models/AmalioLG15
fatcat:4t4fbdapjrb7rb24hne5dmt77q
Distributed Hybrid CPU and GPU training for Graph Neural Networks on Billion-Scale Graphs
[article]
2022
arXiv
pre-print
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It takes only 5-10 seconds to complete an epoch on graphs with hundreds of millions of vertices on a cluster with 64 GPUs. ...
To ensure data locality and load balancing, DistDGLv2 partitions heterogeneous graphs by using a multi-level partitioning algorithm with min-edge cut and multiple balancing constraints. ...
So far, few frameworks are designed to handle heterogeneous graphs with more than one vertex type and edge type. ...
arXiv:2112.15345v3
fatcat:d6xecdcoxnc6bpn2xsan3cugim
Clustering attributed graphs: models, measures and methods
[article]
2015
arXiv
pre-print
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Graph clustering and community detection have traditionally focused on graphs without attributes, with the notable exception of edge weights. ...
We refer to these models as attributed graphs. Consequently, existing graph clustering methods have been recently extended to deal with node and edge attributes. ...
and nodes belonging to multiple clusters depending on the edge type. ...
arXiv:1501.01676v1
fatcat:anagdhywq5adzelcyvgxkqchza
Clustering attributed graphs: Models, measures and methods
2015
Network Science
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Graph clustering and community detection have traditionally focused on graphs without attributes, with the notable exception of edge weights. ...
We refer to these models asattributed graphs. Consequently, existing graph clustering methods have been recently extended to deal with node and edge attributes. ...
While the majority of graph clustering methods partition nodes into disjoint sets, many authors have pointed out that in real contexts individuals often belong to multiple communities. ...
doi:10.1017/nws.2015.9
fatcat:5dqvbld4rzc4bhy2owgtlsom2q
Community Detection in Multi-Layer Graphs
2015
SIGMOD record
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Community detection, also known as graph clustering, has been extensively studied in the literature. ...
The multiple aspects of interactions can be modeled as a multi-layer graph comprised of multiple interdependent graphs, where each graph represents an aspect of the interactions. ...
This approach consists of four steps: creating content edges, combining edges, sampling edges with bias, and clustering. ...
doi:10.1145/2854006.2854013
fatcat:fvcjjhdsxre2voqdowbeond3te
Higher-order Spectral Clustering for Heterogeneous Graphs
[article]
2018
arXiv
pre-print
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Existing work in higher-order clustering has focused on simple homogeneous graphs with a single node/edge type. ...
However, heterogeneous graphs consisting of nodes and edges of different types are seemingly ubiquitous in the real-world. ...
As an aside, existing higher-order clustering methods that extend modularity [10] and conductance [14, 85] are all designed for homogeneous graphs with a single node/edge type and are also based on ...
arXiv:1810.02959v1
fatcat:ouvn5k33avdx5oorx5kr5yvskm
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