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From Community to Role-based Graph Embeddings
[article]
2019
arXiv
pre-print
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Recently, the notion of roles has become increasingly important and has gained a lot of attention due to the proliferation of work on learning representations (node/edge embeddings) from graphs that preserve ...
These mechanisms are typically easy to identify and can help researchers quickly determine whether a method is more prone to learn community or role-based embeddings. ...
From Equivalences on the Graph to Equivalences on Embeddings. ...
arXiv:1908.08572v1
fatcat:qxunw5kmqjhobkhl4nzbzkm6vm
Persona2vec: A Flexible Multi-role Representations Learning Framework for Graphs
[article]
2020
arXiv
pre-print
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However, across many domains, it is common to observe pervasively overlapping community structure, where most nodes belong to multiple communities, playing different roles depending on the contexts. ...
Here, we propose persona2vec, a graph embedding framework that efficiently learns multiple representations of nodes based on their structural contexts. ...
In addition, we take an approach of considering persona-based learning as fine-tuning of the base graph embedding, achieving both efficiency and balance between information from the original graph and ...
arXiv:2006.04941v2
fatcat:d76pxscjebdnbmafiso2cucalu
Persona2vec: a flexible multi-role representations learning framework for graphs
2021
PeerJ Computer Science
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However, across many domains, it is common to observe pervasively overlapping community structure, where most nodes belong to multiple communities, playing different roles depending on the contexts. ...
Here, we propose persona2vec, a graph embedding framework that efficiently learns multiple representations of nodes based on their structural contexts. ...
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. ...
doi:10.7717/peerj-cs.439
pmid:33834106
pmcid:PMC8022511
fatcat:fmcqkcxhbzau7grtjjoispryya
HONE: Higher-Order Network Embeddings
[article]
2018
arXiv
pre-print
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This paper describes a general framework for learning Higher-Order Network Embeddings (HONE) from graph data based on network motifs. ...
In all cases, HONE outperforms recent embedding methods that are unable to capture higher-order structures with a mean relative gain in AUC of 19% (and up to 75% gain) across a wide variety of networks ...
Over the previous decade, there have been many role-based embedding (role discovery) methods that automatically learn node embeddings from graphs; see [47] for a survey. ...
arXiv:1801.09303v2
fatcat:b6e7l3jhk5gvhigl6muldyuuyi
A Survey on Role-Oriented Network Embedding
[article]
2021
arXiv
pre-print
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., role-based similarity, which is usually complementary and completely different from the proximity. ...
However, compared to community-oriented NE problem, there are only a few role-oriented embedding approaches proposed recently. ...
Similar to the taxonomy of community oriented network embedding, we divide these into three categories, low-rank matrix factorization, random walk based and deep learning methods from the first level. ...
arXiv:2107.08379v1
fatcat:hoiso2ugyrdermzcafrscitsfq
Representation Learning on Graphs: Methods and Applications
[article]
2018
arXiv
pre-print
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However, recent years have seen a surge in approaches that automatically learn to encode graph structure into low-dimensional embeddings, using techniques based on deep learning and nonlinear dimensionality ...
Machine learning on graphs is an important and ubiquitous task with applications ranging from drug design to friendship recommendation in social networks. ...
or structural roles played by different nodes, rather than just community structure. ...
arXiv:1709.05584v3
fatcat:brq7dq4u55bi5hzs4xsg3ka6ry
DP-GCN: Node Classification Based on Both Connectivity and Topology Structure Convolutions for Risky Seller Detection
[article]
2021
arXiv
pre-print
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We note that many existing solutions for graph-based node classification only consider node connectivity but not the similarity between node's local topology structure. ...
Such local topology structures reveal sellers' business roles, eg., supplier, drop-shipper, or retailer. ...
It is intuitive that different types of node information
𝑘 (from 2 to 7) to build KNN graph. ...
arXiv:2112.04757v1
fatcat:w2fozgyeqjfv5bsh7gi4ohg564
Temporal Analysis of Reddit Networks via Role Embeddings
[article]
2019
arXiv
pre-print
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In particular, we analyse temporal role embeddings from an individual and a community-level perspective for both loyal and vagrant communities present on Reddit. ...
Inspired by diachronic word analysis from the field of natural language processing, we propose an approach for uncovering temporal insights regarding user roles from social networks using graph embedding ...
ACKNOWLEDGMENTS e authors would like to thank Leonardo F.R. Ribeiro for having a public implementation of struc2vec available and William L. ...
arXiv:1908.05192v1
fatcat:y2ota5sa6bai5puetkbm7cfdcu
Role action embeddings: scalable representation of network positions
[article]
2018
arXiv
pre-print
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We consider the question of embedding nodes with similar local neighborhoods together in embedding space, commonly referred to as "role embeddings." ...
These techniques can be easily combined with existing GNN methods to create unsupervised role embeddings at scale. ...
ACKNOWLEDGEMENTS Thanks to Nima Noorshams, Katerina Marazopoulou, Lada Adamic, Saurabh Verma, Alex Dow, Shaili Jain, and Alex Pesyakhovich for discussions and suggestions. ...
arXiv:1811.08019v2
fatcat:r6ttyp6ws5bjjmbu43scnoxhpe
struc2gauss: Structural role preserving network embedding via Gaussian embedding
2020
Data mining and knowledge discovery
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Network embedding (NE) is playing a principal role in network mining, due to its ability to map nodes into efficient low-dimensional embedding vectors. ...
In this paper, we propose a new NE framework, struc2gauss, which learns node representations in the space of Gaussian distributions and performs network embedding based on global structural information ...
Different from previous studies which focused on community structures, our approach aims to preserve the global role structures. ...
doi:10.1007/s10618-020-00684-x
fatcat:f5ad4xjetzaifargiq7rekifba
ExEm: Expert Embedding using dominating set theory with deep learning approaches
[article]
2021
arXiv
pre-print
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To perform the analysis, graph embedding techniques have emerged as an effective and promising tool. Graph embedding attempts to represent graph nodes as low-dimensional vectors. ...
In this paper, we propose a graph embedding method, called ExEm, that uses dominating-set theory and deep learning approaches to capture node representations. ...
Our first proposed approach is a random walk based graph embedding technique, called ExEm, that incorporates the dominating set from graph theory to graph embedding. ...
arXiv:2001.08503v2
fatcat:zolbpnm5draixbt3zbfy7hgena
Graph Convolutional Network-based Suspicious Communication Pair Estimation for Industrial Control Systems
[article]
2020
arXiv
pre-print
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To solve this problem, we developed a graph convolutional network-based suspicious communication pair estimation using relational graph convolution networks, and evaluated its performance. ...
To reduce false positives due to a simple whitelist-based judgment, we propose a new framework for scoring communications to judge whether the communications not present in whitelists are normal or anomalous ...
Many graph-embedding methods are designed to preserve this nature [14] . A first-order proximity to node v j from a view of node v i is higher if more edges from v i to v j exist. ...
arXiv:2007.10204v1
fatcat:czxixqj6offr7o7wpxeonwae2a
Inferring Users' Social Roles with a Multi-Level Graph Neural Network Model
2021
Entropy
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In this paper, we consider what social network structures reflect users' social statuses and roles since social networks are designed to connect people. ...
Previous studies on this topic have mainly focused on using the categorical, textual and topological data of a social network to predict users' social statuses and roles. ...
A user's social role varies from one social networks to another. ...
doi:10.3390/e23111453
pmid:34828151
pmcid:PMC8625403
fatcat:v7m3muw52zbexc7adxbp4b3fvq
Influence of Random Walk Parametrization on Graph Embeddings
[chapter]
2020
Lecture Notes in Computer Science
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Network or graph embedding has gained increasing attention in the research community during the last years. ...
In particular, many methods to create graph embeddings using random walk based approaches have been developed. node2vec [10] introduced means to control the random walk behavior, guiding the walks. ...
Community structure in a graph is based on proximity, i.e., nodes that are close together belong to a community. ...
doi:10.1007/978-3-030-45442-5_8
fatcat:uehneneiinbafmdygq5ve7qm4e
Is a Single Embedding Enough? Learning Node Representations that Capture Multiple Social Contexts
2019
The World Wide Web Conference on - WWW '19
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2019 pre-print arXiv:1905.02138v1 fatcat:b5t3phdzwrhk5h7uuk4aswvvii
Based on a principled decomposition of the ego-network, each representation encodes the role of the node in a different local community in which the nodes participate. ...
on a variety of graphs, reducing the error by up to 90%. ...
[47] uses a modularity based community detection model to jointly optimize the embedding and community assignment of each node. ...
doi:10.1145/3308558.3313660
dblp:conf/www/EpastoP19
fatcat:vn5kqq6gkzaujbafk5felst4ay
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