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Recent Advances in Network-based Methods for Disease Gene Prediction [article]

Sezin Kircali Ata, Min Wu, Yuan Fang, Le Ou-Yang, Chee Keong Kwoh, Xiao-Li Li
2020 arXiv   pre-print
Secondly, we categorize existing network-based efforts into network diffusion methods, traditional machine learning methods with handcrafted graph features and graph representation learning methods.  ...  To provide the researchers with alternative low-cost disease-gene association evidence, computational approaches come into play.  ...  In addition, a multi-view or multiplex graph is a collection of graphs with the same set of nodes and different types of edges (e.g., edges from different views).  ... 
arXiv:2007.10848v1 fatcat:zhrspbsj6zfpfhwa42mzjp4lvy

Ensemble Node Embeddings using Tensor Decomposition: A Case-Study on DeepWalk

Jia Chen, Evangelos E. Papalexakis
2020 2020 International Conference on Data Mining Workshops (ICDMW)  
In this context, we propose a new ensemble node embedding approach, called TENSEMBLE2VEC, by first generating multiple embeddings using the existing techniques and taking them as multiview data input of  ...  Node embeddings have been attracting increasing attention during the past years.  ...  are our ensemble node embeddings.  ... 
doi:10.1109/icdmw51313.2020.00080 fatcat:zkxi54u26raapc5ydnxws6ibfi

Ensemble Node Embeddings using Tensor Decomposition: A Case-Study on DeepWalk [article]

Jia Chen, Evangelos E. Papalexakis
2020 arXiv   pre-print
In this context, we propose a new ensemble node embedding approach, called TenSemble2Vec, by first generating multiple embeddings using the existing techniques and taking them as multiview data input of  ...  Node embeddings have been attracting increasing attention during the past years.  ...  are our ensemble node embeddings.  ... 
arXiv:2008.07672v1 fatcat:7kg2tdu4uff2posatpzqkj5czi

Multi-view Graph Contrastive Representation Learning for Drug-Drug Interaction Prediction [article]

Yingheng Wang, Yaosen Min, Xin Chen, Ji Wu
2020 arXiv   pre-print
MIRACLE treats a DDI network as a multi-view graph where each node in the interaction graph itself is a drug molecular graph instance.  ...  This study presents a new method, multi-view graph contrastive representation learning for drug-drug interaction prediction, MIRACLE for brevity, to capture inter-view molecule structure and intra-view  ...  In order to encode the intra-view interaction information into the drug embeddings, we establish an encoder to integrate the multi-view network information.  ... 
arXiv:2010.11711v2 fatcat:ryzrp7rosnblhc3bpcgnrmvxp4

Multi-Level Network Embedding with Boosted Low-Rank Matrix Approximation [article]

Jundong Li, Liang Wu, Huan Liu
2018 arXiv   pre-print
In this regard, we propose a novel multi-level network embedding framework BoostNE, which can learn multiple network embedding representations of different granularity from coarse to fine without imposing  ...  The proposed BoostNE method is also in line with the successful gradient boosting method in ensemble learning as multiple weak embeddings lead to a stronger and more effective one.  ...  (Multi-Level Network Embedding): Given a network G = (V, E) with n nodes, a predefined embedding dimensionality d and the number of levels k, the problem of multi-level network embedding is to learn a  ... 
arXiv:1808.08627v1 fatcat:2vtmmbcwwnbfzmqpxb6h66ibk4

MARINE: Multi-relational Network Embeddings with Relational Proximity and Node Attributes

Noriaki Kawamae
2019 The World Wide Web Conference on - WWW '19  
We also extend the framework to incorporate existing features of nodes in a graph, which can further be exploited for the ensemble of embedding.  ...  We observe that there are two diverse branches of network embedding: for homogeneous graphs and for multi-relational graphs.  ...  The idea can be modeled by the inner product: (x j − x i ) ⊤ t k . (6) In real-world multi-relational networks, similar type of nodes might connect to similar types of others via similar relation (e.g.  ... 
doi:10.1145/3308558.3313715 dblp:conf/www/Kawamae19 fatcat:mt5o5joidvgapgmf4lain3t3be

HRGE-Net: Hierarchical Relational Graph Embedding Network for Multi-view 3D Shape Recognition [article]

Xin Wei, Ruixuan Yu, Jian Sun
2019 arXiv   pre-print
We construct a relational graph with multi-view images as nodes, and design relational graph embedding by modeling pairwise and neighboring relations among views.  ...  By gradually coarsening the graph, we build a hierarchical relational graph embedding network (HRGE-Net) to aggregate the multi-view features to be a global shape descriptor.  ...  The method of ReVGG extracts multi-view features by a reduced VGG-M network, and defines similarity with modified Neighbor Set Similarity.  ... 
arXiv:1908.10098v1 fatcat:mvf4f7z7mzeltadlfjo2wdir6y

Visual Analytics of Simulation Ensembles for Network Dynamics

Quynh Quang Ngo, Marc-Thorsten Hütt, Lars Linsen
2019 International Symposium on Vision, Modeling, and Visualization  
We relate the nodes' topological structures to their dynamical similarity in a 2D plot based on an interactively defined hierarchy of topological properties and a 1D embedding for the dynamical similarity  ...  To answer this question, we are analyzing ensembles of simulation runs with different parameter settings executed on a given network topology.  ...  structures to dynamical node similarities. • A parameter-space visualization that reveals topology-dynamics matches at an overview level and allows for coordinated interaction with more detailed views  ... 
doi:10.2312/vmv.20191322 dblp:conf/vmv/NgoHL19 fatcat:u6xntc4tx5ekxn3ns5qguqxwsy

A Visual Analytics Approach for Traffic Flow Prediction Ensembles

Kezhi Kong, Yuxin Ma, Chentao Ye, Junhua Lu, Xiqun Chen, Wei Zhang, Wei Chen
2018 Pacific Conference on Computer Graphics and Applications  
The variations of space, time, and network structures of those results are presented with the visualization designs.  ...  The visual interface provides a suite of interactions to enhance exploration of the ensembles. With the system, analysts can discover some intrinsic patterns in the ensemble.  ...  view of a set of similar ensembles.  ... 
doi:10.2312/pg.20181281 dblp:conf/pg/KongMYLCZC18 fatcat:yhyjbafxzbhd3gyyuekr6d2vey

A Review of Approaches for Predicting Drug–Drug Interactions Based on Machine Learning

Ke Han, Peigang Cao, Yu Wang, Fang Xie, Jiaqi Ma, Mengyao Yu, Jianchun Wang, Yaoqun Xu, Yu Zhang, Jie Wan
2022 Frontiers in Pharmacology  
However, they may also cause adverse reactions in patients, with serious consequences.  ...  For modeling, each drug was a node in the drug association network, which was extended by the GCN to embed the characteristics and edges of the multi-view node.  ...  Ma et al. (2018) considered each type of drug characteristic as a view, calculated the similarity of each view, and then used a multi-view graphical automatic encoder to integrate drug similarity.  ... 
doi:10.3389/fphar.2021.814858 pmid:35153767 pmcid:PMC8835726 fatcat:6nhmla6cubbltieol2jhgw5zum

Option Predictive Clustering Trees for Multi-label Classification

Tomaž Stepišnik, Dragi Kocev, Sašo Džeroski
2020 Acta Polytechnica Hungarica  
In this work, we focus on the task of multi-label classification (MLC), where every example is associated with a set of labels.  ...  Results show that OPCTs as ensembles can achieve performance similar to the bagging ensembles of PCTs, while the single trees extracted from OPCTs can outperform standard PCTs.  ...  This is done in the same way in both PCTs (and ensembles thereof) and OPCTs, therefore, similar number of nodes in the trees indicates similar time needed for their induction.  ... 
doi:10.12700/aph.17.10.2020.10.7 fatcat:7dkcrggsbjbvhiy2xqpskrt6ra

A Comprehensive Survey on Community Detection with Deep Learning [article]

Xing Su, Shan Xue, Fanzhen Liu, Jia Wu, Jian Yang, Chuan Zhou, Wenbin Hu, Cecile Paris, Surya Nepal, Di Jin, Quan Z. Sheng, Philip S. Yu
2021 arXiv   pre-print
Despite the classical spectral clustering and statistical inference methods, we notice a significant development of deep learning techniques for community detection in recent years with their advantages  ...  The main category, i.e., deep neural networks, is further divided into convolutional networks, graph attention networks, generative adversarial networks and autoencoders.  ...  Considering multi-view attributes in networks, Multi-View Attribute Graph Convolution Networks (MAGCN) [118] designs a two-pathway encoder: the first pathway encodes with a multi-view attribute GAT capable  ... 
arXiv:2105.12584v2 fatcat:matipshxnzcdloygrcrwx2sxr4

DemiNet: Dependency-Aware Multi-Interest Network with Self-Supervised Graph Learning for Click-Through Rate Prediction [article]

Yule Wang, Qiang Luo, Yue Ding, Dong Wang, Hongbo Deng
2021 arXiv   pre-print
Secondly, for multiple interests extraction, multi-head attention is conducted on top of the graph embedding.  ...  In this paper, we propose a novel model named DemiNet (short for DEpendency-Aware Multi-Interest Network}) to address the above two issues.  ...  havior sequence, we perform multi-dependency-aware heterogeneous attention and self-supervised interest learning.  ... 
arXiv:2109.12512v1 fatcat:vznmni5xfzhsnjkfnxh5kfyhsa

Enriching Complex Networks with Word Embeddings for Detecting Mild Cognitive Impairment from Speech Transcripts

Leandro Santos, Edilson Anselmo Corrêa Júnior, Osvaldo Oliveira Jr, Diego Amancio, Letícia Mansur, Sandra Aluísio
2017 Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)  
Taken together, the results indicate that complex networks enriched with embedding is promising for detecting MCI in large-scale assessments.  ...  In this paper, we modeled transcripts into complex networks and enriched them with word embedding (CNE) to better represent short texts produced in neuropsychological assessments.  ...  We also combined these classifiers through ensemble and multi-view learning.  ... 
doi:10.18653/v1/p17-1118 dblp:conf/acl/SantosJJAMA17 fatcat:hzgtt7z465cw3obxj2ux4m7zq4

Enriching Complex Networks with Word Embeddings for Detecting Mild Cognitive Impairment from Speech Transcripts [article]

Leandro B. dos Santos, Edilson A. Corrêa Jr, Osvaldo N. Oliveira Jr, Diego R. Amancio, Letícia L. Mansur, Sandra M. Aluísio
2017 arXiv   pre-print
Taken together, the results indicate that complex networks enriched with embedding is promising for detecting MCI in large-scale assessments  ...  In this paper, we modeled transcripts into complex networks and enriched them with word embedding (CNE) to better represent short texts produced in neuropsychological assessments.  ...  We also combined these classifiers through ensemble and multi-view learning.  ... 
arXiv:1704.08088v1 fatcat:yph46xuokrdhhldij2g2pj2cru
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