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Feature Importance-aware Graph Attention Network and Dueling Double Deep Q-Network Combined Approach for Critical Node Detection Problems [article]

Xuwei Tan, Yangming Zhou, Zhang-Hua Fu, Mengchu Zhou
2021 arXiv   pre-print
Detecting critical nodes in sparse networks is important in a variety of application domains.  ...  This work proposes a feature importance-aware graph attention network for node representation and combines it with dueling double deep Q-network to create an end-to-end algorithm to solve CNP for the first  ...  Zhao et al. (2020) designed a deep learning model called InfGCN to identify the most influential nodes in a complex network based on GCNs.  ... 
arXiv:2112.03404v1 fatcat:h5lnx2qcfvaqbgk76cosx6w3gy

StructPool: Structured Graph Pooling via Conditional Random Fields

Hao Yuan, Shuiwang Ji
2020 International Conference on Learning Representations  
Learning high-level representations for graphs is of great importance for graph analysis tasks. In addition to graph convolution, graph pooling is an important but less explored research area.  ...  We consider the graph pooling as a node clustering problem, which requires the learning of a cluster assignment matrix.  ...  ACKNOWLEDGEMENT This work was supported in part by National Science Foundation grants DBI-1661289 and IIS-1908198.  ... 
dblp:conf/iclr/YuanJ20 fatcat:ck5lbigj4rcmfpdb3j7qsdpabu

Autonomous Deep Learning: Continual Learning Approach for Dynamic Environments [article]

Andri Ashfahani, Mahardhika Pratama
2019 arXiv   pre-print
A deep continual learning algorithm, namely autonomous deep learning (ADL), is proposed in this paper.  ...  Unlike traditional deep learning methods, ADL features a flexible structure where its network structure can be constructed from scratch with the absence of an initial network structure via the self-constructing  ...  Proposed Methods A fully elastic deep neural network (DNN), namely Autonomous Deep Learning (ADL), is proposed in this paper.  ... 
arXiv:1810.07348v2 fatcat:5eavov7icnagdmbgzhaf7k3x2a

Placement Optimization with Deep Reinforcement Learning [article]

Anna Goldie, Azalia Mirhoseini
2020 arXiv   pre-print
Placement Optimization is an important problem in systems and chip design, which consists of mapping the nodes of a graph onto a limited set of resources to optimize for an objective, subject to constraints  ...  In this paper, we start by motivating reinforcement learning as a solution to the placement problem. We then give an overview of what deep reinforcement learning is.  ...  ACKNOWLEDGMENTS We would like to thank our amazing collaborators on deep reinforcement learning for placement research, including Ebrahim Songhori, Joe Jiang, Shen Wang, Hieu Pham, Yanqi Zhou, Will Hang  ... 
arXiv:2003.08445v1 fatcat:msjrsgq4ava4xfnaduhak7ocz4

Hyperparameter Tuning for Deep Reinforcement Learning Applications [article]

Mariam Kiran, Melis Ozyildirim
2022 arXiv   pre-print
In comparison to other neural network architectures, deep RL has not witnessed much hyperparameter tuning, due to its algorithm complexity and simulation platforms needed.  ...  Our results are imperative to advance deep reinforcement learning controllers for real-world problems.  ...  as Deep Q-Network (DQN), Deep Deterministic Policy Gradient (DDPG), to name a few [3] ) or just simple allowing the deep RL to learn via trial-and-error.  ... 
arXiv:2201.11182v1 fatcat:ilhx5djtlzbcdcohcax6mj5dda

Selective Network Discovery via Deep Reinforcement Learning on Embedded Spaces [article]

Peter Morales, Rajmonda Sulo Caceres, Tina Eliassi-Rad
2020 arXiv   pre-print
We propose a framework, called Network Actor Critic (NAC), which learns a policy and notion of future reward in an offline setting via a deep reinforcement learning algorithm.  ...  Complex networks are often either too large for full exploration, partially accessible, or partially observed. Downstream learning tasks on these incomplete networks can produce low quality results.  ...  Reinforcement learning for tasks on complex networks is a relatively new perspective.  ... 
arXiv:1909.07294v2 fatcat:ogmaukqkgrb7foyptitksvjn4e

A Survey on the Recent Advances of Deep Community Detection

Stavros Souravlas, Sofia Anastasiadou, Stefanos Katsavounis
2021 Applied Sciences  
In this paper, we present the recent advances of deep learning techniques for community detection.  ...  In the first days of social networking, the typical view of a community was a set of user profiles of the same interests and likes, and this community kept enlarging by searching, proposing, and adding  ...  Two new approaches can be found in [50, 51] . Sperli's approach [50] is based on deep learning approaches and on the topological properties of social networks.  ... 
doi:10.3390/app11167179 fatcat:lzff6bskjrfgfo5ho7dalltke4

Deep Generative Models for Generating Labeled Graphs

Shuangfei Fan, Bert Huang
2019 International Conference on Learning Representations  
As a new way to train generative models, generative adversarial networks (GANs) have achieved considerable success in image generation, and this framework has also recently been applied to data with graph  ...  We identify the drawbacks of existing deep frameworks for generating graphs, and we propose labeledgraph generative adversarial networks (LGGAN) to train deep generative models for graph-structured data  ...  ACKNOWLEDGEMENT The authors would like to thank NVIDIA for donating hardware and Amazon for donating cloud computing credits to our group, we appreciate their support to our research.  ... 
dblp:conf/iclr/FanH19 fatcat:jhgn2gqdmvhdjjw7yzkfcubboq

DeepSNEM: Deep Signaling Network Embeddings for compound mechanism of action identification [article]

Christos Fotis, George Alevizos, Nikolaos Meimetis, Christina Koleri, Thomas Gkekas, Leonidas G. Alexopoulos
2021 bioRxiv   pre-print
Furthermore, we developed a novel unsupervised graph deep learning pipeline, called deepSNEM, to encode the information in the compound-induced signaling networks in fixed-length high-dimensional representations  ...  In order to take into account the complexity of the biological system, several computational methods have been developed that utilize prior knowledge of molecular interactions to create a signaling network  ...  There have been many studies for the development of deep learning models for graph data in a variety of fields.  ... 
doi:10.1101/2021.11.29.470365 fatcat:4p5mhzb5gbcajgmz6ijg5pqaq4

Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking [article]

Aleksandar Bojchevski, Stephan Günnemann
2018 arXiv   pre-print
Methods that learn representations of nodes in a graph play a critical role in network analysis since they enable many downstream learning tasks.  ...  Unlike most approaches that represent nodes as point vectors in a low-dimensional continuous space, we embed each node as a Gaussian distribution, allowing us to capture uncertainty about the representation  ...  ACKNOWLEDGMENTS This research was supported by the German Research Foundation, Emmy Noether grant GU 1409/2-1, and by the Technical University of Munich -Institute for Advanced Study, funded by the German  ... 
arXiv:1707.03815v4 fatcat:terkojmlkze4bm3hqbn65airjq

Deep generative models in DataSHIELD [article]

Stefan Lenz, Harald Binder
2020 arXiv   pre-print
Our implementation adds to DataSHIELD the ability to generate artificial data that can be used for various analyses, e. g. for pattern recognition with deep learning.  ...  The DataSHIELD software provides an infrastructure and a set of statistical methods for joint analyses of distributed data.  ...  Acknowledgements This work has been supported by the Federal Ministry of Education and Research (BMBF) in Germany in the MIRACUM project (FKZ 01ZZ1801B).  ... 
arXiv:2003.07775v1 fatcat:f6a6b3vjezblddc6gbaermcflq

Continual learning via inter-task synaptic mapping

Mao Fubing, Weng Weiwei, Mahardhika Pratama, Edward Yapp Kien Yee
2021 Knowledge-Based Systems  
new concepts, these approaches do not exploit common information of each task which can be shared to existing neurons.  ...  An Inter-Task Synaptic Mapping (ISYANA) is proposed here to underpin knowledge retention for continual learning.  ...  This work was mainly done when the first author was a research fellow in NTU.  ... 
doi:10.1016/j.knosys.2021.106947 fatcat:ovaf2wtvordqhnwzr7c4r6paaq

A Survey of Community Detection Approaches: From Statistical Modeling to Deep Learning [article]

Di Jin, Zhizhi Yu, Pengfei Jiao, Shirui Pan, Dongxiao He, Jia Wu, Philip S. Yu, Weixiong Zhang
2021 arXiv   pre-print
model and deep learning.  ...  utilize deep learning and convert networked data into low dimensional representation.  ...  Deep learning-based methods aim to identify community structures utilizing a new type of communityoriented network representation.  ... 
arXiv:2101.01669v3 fatcat:p2lkjuslmzd6hc6kpum3sz5xwq

Deep learning based network similarity for model selection

Kushal Veer Singh, Ajay Kumar Verma, Lovekesh Vig, Michael Maes
2021 Data Science  
Capturing data in the form of networks is becoming an increasingly popular approach for modeling, analyzing and visualising complex phenomena, to understand the important properties of the underlying complex  ...  To overcome these limitations, we considered a broad array of network features, with the aim of representing different structural aspects of the network and employed deep learning techniques such as deep  ...  ., India for providing the research fellowship to K.V.S. and A.K.V.  ... 
doi:10.3233/ds-210033 fatcat:srmlzbxmlrafxp5wslbcwvyoz4

A Review on Community Detection in Large Complex Networks from Conventional to Deep Learning Methods: A Call for the Use of Parallel Meta-Heuristic Algorithms

Mohammed Al-Andoli, Shing Chiang Tan, Wooi Ping Cheah, Sin Yin Tan
2021 IEEE Access  
Recently, in [80] , the authors developed a new learning method for CD in CNs.  ...  .: A Review on Community Detection in Large Complex Networks from Conventional to Deep Learning Methods VOLUME XX, 2021  ... 
doi:10.1109/access.2021.3095335 fatcat:4zggvxofqvbcjbwylk7swc3c34
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