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SNE: Signed Network Embedding
[article]
2017
arXiv
pre-print
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However, these models based on skip-gram cannot be applied to signed networks because they can only deal with one type of link. ...
We conduct two experiments, node classification and link prediction, on both directed and undirected signed networks and compare with four baselines including a matrix factorization method and three state-of-the-art ...
The log-bilinear model then trains word embeddings v and position weight vectors c by optimizing the objective function similar to the skip-gram. ...
arXiv:1703.04837v1
fatcat:rfwrq45aubfbxajqwkuxc3i4bq
Supervised Random Walks: Predicting and Recommending Links in Social Networks
[article]
2010
arXiv
pre-print
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We formulate a supervised learning task where the goal is to learn a function that assigns strengths to edges in the network such that a random walker is more likely to visit the nodes to which new links ...
We develop an efficient training algorithm to directly learn the edge strength estimation function. ...
Co-authorship networks. ...
arXiv:1011.4071v1
fatcat:e3rkkcrhirgxvcixmhscxmxpxe
Feature Hashing for Network Representation Learning
2018
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
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More specifically, we firstly derive a proximity measurement called expected distance as target which combines position distribution and co-occurrence statistics of nodes over random walks so as to build ...
There are two main mapping functions in this framework. The first is an encoder to map each node into high-dimensional vectors. ...
In such function, we define a proximity measurement called expected distance which combines the pairwise position distribution and the co-occurrence statistics between nodes over random walks. ...
doi:10.24963/ijcai.2018/390
dblp:conf/ijcai/WangWG018
fatcat:fvnu43y5nnbsdbhkyj6immusfe
Supervised random walks
2011
Proceedings of the fourth ACM international conference on Web search and data mining - WSDM '11
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We formulate a supervised learning task where the goal is to learn a function that assigns strengths to edges in the network such that a random walker is more likely to visit the nodes to which new links ...
We develop an efficient training algorithm to directly learn the edge strength estimation function. ...
Co-authorship networks. ...
doi:10.1145/1935826.1935914
dblp:conf/wsdm/BackstromL11
fatcat:5glee4eo5rfbfb6sug5vxifvnm
Learning Multigraph Node Embeddings Using Guided Lévy Flights
[chapter]
2020
Lecture Notes in Computer Science
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The transition probabilities are learned in a supervised fashion as a function of node attributes (metadata based and/or network structure based). ...
for learning multigraph network representation. ...
In a scientific network, researchers can share a link by virtue of being co-authors on a paper or by citing each other's works. ...
doi:10.1007/978-3-030-47426-3_41
fatcat:wv65mhiuyfgiblswvubpsguuqy
Exploring chromatin conformation and gene co-expression through graph embedding
2020
Bioinformatics
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representation of each gene from the interaction network; finally, we train a classifier on gene embedding pairs to predict if they are co-expressed. ...
We first define a gene chromatin interaction network where each gene is associated to its physical interaction profile; then, we apply two graph embedding techniques to extract a low-dimensional vector ...
For the training of the random forest classifier, we sampled a number of inter-chromosomal links from the whole-genome co-expression network equal to the total number of intra-chromosomal links. ...
doi:10.1093/bioinformatics/btaa803
pmid:33381846
fatcat:3hlmoep64ze3vnauodyk74pkrm
Layer Information Similarity Concerned Network Embedding
2021
Complexity
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Firstly, we introduce the common vector for each node shared by all layers and layer vectors for each layer where common vectors obtain the overall structure of the multiplex network and layer vectors ...
To evaluate our proposed model, we conduct node classification and link prediction tasks to verify the effectiveness of our model, and the results show that LISCNE can achieve better or comparable performance ...
Performance on Link Prediction. For single-layer methods, we train the node embedding for each layer and use it to predict links in the corresponding layer. ...
doi:10.1155/2021/2260488
doaj:96847eb8600244d5920fef38d23c1e97
fatcat:uzdeghl3gjg3zepjm7ggk7lo4a
Network representation learning: models, methods and applications
2019
SN Applied Sciences
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attribute vector of node i. ...
Definition 5 A signed network is a network G = (V , E) , v ∈ V , e ∈ E and for each edge, e ij = +1 or e ij = −1 , denoting a positive link or a negative link between v i and v j . ...
Author citation network links authors when one author cites the other, paper citation network links papers when one paper cites the other and co-authorship network links authors if they co-author at least ...
doi:10.1007/s42452-019-1044-9
fatcat:zvlbj4qozzfw3dxoyevb6wgska
Principled Multilayer Network Embedding
[article]
2017
arXiv
pre-print
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network into a continuous vector space. ...
From the evaluation, we have proved that comparing with regular link prediction methods, "layer co-analysis" achieved the best performance on most of the datasets, while "network aggregation" and "results ...
The mapping function f is defined by training on the merged network G using node2vec. ...
arXiv:1709.03551v3
fatcat:xa2tptlng5dmdaka3hyxdiy6vy
Exponential Family Graph Embeddings
[article]
2019
arXiv
pre-print
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Representing networks in a low dimensional latent space is a crucial task with many interesting applications in graph learning problems, such as link prediction and node classification. ...
We introduce the generic exponential family graph embedding model, that generalizes random walk-based network representation learning techniques to exponential family conditional distributions. ...
that co-occur within a random walk. ...
arXiv:1911.09007v1
fatcat:lgwiuh3zuffznb7rdsnf2cghre
Network Embedding For Link Prediction in Bipartite Networks
2021
European Journal of Science and Technology
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Random Forest models trained with embedding vectors obtained from BiNE method achieved the highest performances. ...
Network embedding, which maps each node in the network to a low-dimensional feature vector is used to solve many problems. ...
Therefore, similarity between two nodes u and v is defined as the probability of their co-occurrence on a random walk through the network. ...
doi:10.31590/ejosat.937722
fatcat:fgl3ran6lzdfxps3guzuqddu2i
Community aware random walk for network embedding
2018
Knowledge-Based Systems
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2017 pre-print arXiv:1710.05199v1 fatcat:7uevecxqbrey7byndoacrzysbmOther Versions
Social network analysis provides meaningful information about behavior of network members that can be used for diverse applications such as classification, link prediction. ...
CARE builds customized paths, which are consisted of local and global structure of network nodes, as a basis for network embedding and uses the Skip-gram model to learn representation vector of nodes. ...
Wikipedia: As another evaluation, we test CARE on co-occurrence word network of Wikipedia articles. ...
doi:10.1016/j.knosys.2018.02.028
fatcat:bx76vmee75da3knb2t4ahaikvu
Paper2vec: Citation-Context Based Document Distributed Representation for Scholar Recommendation
[article]
2017
arXiv
pre-print
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Despite of the success of previous approaches, they are, however, based on co-occurrence of items. Once there are no co-occurrence items available in documents, they will not work well. ...
After that we explore a variant of matrix factorization approach to train distributed representations of papers on the matrix, and leverage the distributed representations to measure similarities of papers ...
When the cost function is satisfied and according to the weight scheme, the exponential of the inner product of the paper vector and the context vector represents the random walk probability of the context ...
arXiv:1703.06587v1
fatcat:ay7e6cydgnexff7wixp7qyudfi
SSNE: Effective Node Representation for Link Prediction in Sparse Networks
[article]
2020
arXiv
pre-print
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In this paper, we propose a model, Sparse Structural Network Embedding (SSNE), to obtain node representation for link predication in sparse networks. ...
Graph embedding is gaining its popularity for link prediction in complex networks and achieving excellent performance. ...
Y 1×n
u
the output vector for node u
i 6: end for 7: Normalizing matrix SP CO h by row, SNHAM= Normal (SP CO h );B. ...
arXiv:2011.07788v1
fatcat:d45vkr3farhnvc6quaqd6o3hi4
Healthy Cognitive Aging: A Hybrid Random Vector Functional-Link Model for the Analysis of Alzheimer's Disease
2017
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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We propose a hybrid pathological analysis model, which integrates manifold learning and Random Vector functional-link network (RVFL) so as to achieve better ability to extract discriminant information ...
Support Vector Machine (SVM), Random Forest (RF), Decision Tree and Multilayer Perceptron (MLP). ...
Random vector functional-link (RVFL) network The idea of functional link network was suggested by Pao and co-workers in 1988 (Klassen, Pao, and Chen 1988) . ...
doi:10.1609/aaai.v31i1.11181
fatcat:4u3a3o7pqzd7lkk7kw5x23q4r4
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