This page is an archive for papers reviewed by members of the Network Science Lab, along with corresponding presentation materials, as part of the NS-CUK Weekly Seminar series. Launched in Fall 2022, this seminar series is intended to be a platform for the members of the Network Science Lab at the Catholic University of Korea to exchange insights and understanding of state-of-the-art AI methodologies and models for graph mining.
Previous Years
2023
Dec 26th, 2023
V.T.Hoang, Review on "Deformable Graph Transformer", arXiv preprint, arXiv:2206.14337, 2022
T.B.T. Do, Review on "node2vec: Scalable Feature Learning for Networks", KDD 2016
M.S.Kim, Review on "EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks", ICML 2019
Dec 18th, 2023
V.T.Hoang, Review on "Towards Deep Attention in Graph Neural Networks: Problems and Remedies", ICML 2023
T.B.T. Do, Review on "DeepWalk: Online Learning of Social Representations", KDD 2014
H.B.Kim, Review on "Knowledge Graph Convolutional Networks for Recommender Systems", WWW 2019
Dec 4th, 2023
V.T.Hoang, Review on "MMKG: Multi-Modal Knowledge Graphs", ESWC 2019
Nov 27th, 2023
V.T.Hoang, Review on "Uncovering the Structural Fairness in Graph Contrastive Learning", NeurIPS 2023
H.B.Kim, Review on "Graph Neural Networks for Social Recommendation", WWW 2019
Y.J.Wu, Review on "LoRA: Low-Rank Adaptation of Large Language Models", ICLR 2022
Nov 20th, 2023
V.T.Hoang, Review on "Imbalanced node classification with Graph Neural Networks: A unified approach leveraging homophily and label information", Applied Soft Computing, 2023
H.B.Kim, Review on "Graph convolutional neural networks for web-scale recommender systems", KDD 2018
Nov 13th, 2023
V.T.Hoang, Review on "RETHINKING THE EXPRESSIVE POWER OF GNNS VIA GRAPH BICONNECTIVITY", ICLR 2023
H.B.Kim, Review on "LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation", SIGIR 2020
Nov 6th, 2023
H.B.Kim, Review on "Do Transformers Really Perform Badly for Graph Representation?", NeurIPS 2021
Y.J.Wu, Review on "Generative Adversarial Networks", NeurIPS 2014
Oct 22nd, 2023
V.T.Hoang, Review on "Exploring attention mechanism for graph similarity learning", Knowledge-Based Systems, 2023
H.B.Kim, Review on "A Generalization of Transformer Networks to Graphs", AAAI 2021
M.S.Kim, Review on "Deep Residual Learning for Image Recognition", CVPR 2016
Oct 21st, 2023
V.T.Hoang, Review on "Conditional Graph Information Bottleneck for Molecular Relational Learning", ICML 2023
H.B.Kim, Review on "Weisfeiler and Leman Go Neural: Higher-order Graph Neural Network", AAAI 2019
Oct 20th, 2023
V.T.Hoang, Review on "Deep Graph Contrastive Representation Learning", arXiv preprint, arXiv:2006.04131, 2020
Y.J.Wu, Review on "Efficientnet: Rethinking model scaling for convolutional neural networks", ICML 2019
Oct 9th, 2023
V.T.Hoang, Review on "Graph Neural Prompting with Large Language Models", arXiv preprint, arXiv:2309.15427, 2023
Oct 2nd, 2023
H.B.Kim, Review on "How To Find Your Friendly Neighborhood: Graph Attention Design With Self-Supervision", ICLR 2023
Sep 25th, 2023
V.T.Hoang, Review on "Automated data augmentations for graph classification", ICLR 2023
J.H.Lee, Review on "Graph Transformer with Graph Pooling for Node Classification", IJCAI 2023
H.B.Kim, Review on "Sparse Graph Attention Networks", IEEE Transactions on Knowledge and Data Engineering, 2021
M.S.Kim, Review on "Going Deeper With Convolutions", CVPR 2015
Sep 18th, 2023
V.T.Hoang, Review on "Learning fair graph representations via automated data augmentations", ICLR 2023
J.H.Lee, Review on "Specformer: Spectral graph neural networks meet transformers", ICLR 2023
H.B.Kim, Review on "Graph Attention Networks", ICLR 2018
M.S.Kim, Review on "Very Deep Convolutional Networks for Large-Scale Image Recognition", ICLR 2015
Sep 11th, 2023
V.T.Hoang, Review on "Edgeformers: Graph-Empowered Transformers for Representation Learning on Textual-Edge Networks", ICLR 2023
J.H.Lee, Review on "Shift-Robust Node Classification via Graph Adversarial Clustering", NeurIPS 2022
H.B.Kim, Review on "DeeperGCN: ALL You Need to Train Deeper GCN", arXiv preprint, arXiv:2006.07739, 2020
H.E.Lee, Review on "Weisfeiler and Leman Go Neural: Higher-Order Graph Neural Networks", AAAI 2019
M.S.Kim, Review on "ImageNet Classification with Deep Convolutional Neural Networks", NeurIPS 2012
Sep 4th, 2023
V.T.Hoang, Review on "GOAT: A Global Transformer on Large-scale Graphs", ICML 2023
J.H.Lee, Review on "Graph Propagation Transformer for Graph Representation Learning", IJCAI 2023
H.B.Kim, Review on "Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks", KDD 20219
H.E.Lee, Review on "Weisfeiler and Leman Go Neural: Higher-Order Graph Neural Networks", AAAI 2019
Aug 28th, 2023
V.T.Hoang, Review on "GRPE: Relative Positional Encoding for Graph Transformer", ICLR 2022
H.B.Kim, Review on "FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling", ICLR 2016
H.E.Lee, Review on "Gated Graph Sequence Neural Networks", ICLR 2016
Aug 21st, 2023
V.T.Hoang, Review on "Augmentation-Free Self-Supervised Learning on Graphs", AAAI 2022
J.H.Lee, Review on "Learnable Structural Semantic Readout for Graph Classification", ICDM 2021
H.E.Lee, Review on "Gated Graph Sequence Neural Networks", ICLR 2016
Aug 14th, 2023
J.H.Lee, Review on "Task Relation-aware Continual User Representation Learning", KDD 2023
H.E.Lee, Review on "PTE: Predictive Text Embedding through Large-scale Heterogeneous Text Networks", KDD 2015
Aug 7th, 2023
J.H.Lee, Review on "Similarity Preserving Adversarial Graph Contrastive Learning", KDD 2023
H.B.Kim, Review on "Inductive Representation Learning on Large Graphs", NeurIPS 2017
H.E.Lee, Review on "PTE: Predictive Text Embedding through Large-scale Heterogeneous Text Networks", KDD 2015
Jul 31st, 2023
V.T.Hoang, Review on "Namkyeong Lee, et al. Relational Self-Supervised Learning on Graphs", CIKM 2022
J.H.Lee, Review on "Relational Self-Supervised Learning on Graphs", CIKM 2022
H.B.Kim, Review on "metapath2vec: Scalable representation learning for heterogeneous networks", KDD 2017
H.E.Lee, Review on "Graph Star Net for Generalized Multi-Task Learning", arXiv preprint, arXiv:1906.12330, 2019
Jul 24th, 2023
V.T.Hoang, Review on "Nested Graph Neural Networks", NeurIPS 2021
J.H.Lee, Review on "Rumor Dectection on Twitter with Claim-Guided Hierarchical Graph Attention Networks", EMNLP 2021
H.B.Kim, Review on "metapath2vec: Scalable representation learning for heterogeneous networks", KDD 2017
H.E.Lee, Review on "Structural Deep Embedding for Hyper-Networks", AAAI 2018
Jul 17th, 2023
V.T.Hoang, Review on "Multi-Order-Content-Based Adaptive Graph Attention Network for Graph Node Classification", Symmetry, 2023
J.H.Lee, Review on "Relational Attention: Generalizing Transformers for Graph-Structured Tasks", ICLR 2023
H.B.Kim, Review on "Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking", ICLR 2018
H.E.Lee, Review on "Structural Deep Embedding for Hyper-Networks", AAAI 2018
Jul 10th, 2023
V.T.Hoang, Review on "Are More Layers Beneficial to Graph Transformers?", ICLR 2023
S.T.Nguyen, Review on "Accurate learning of graph representations with graph multiset pooling", ICLR 2021
J.H.Lee, Review on "Graph Neural Networks with convolutional ARMA filters", IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021
H.B.Kim, Review on "Sequential Recommendation with Graph Neural Networks", SIGIR 2021
Jul 3rd, 2023
V.T.Hoang, Review on "Relphormer: Relational Graph Transformer for Knowledge Graph Representations", arXiv preprint, arXiv:2205.10852, 2022
S.T.Nguyen, Review on "Hierarchical Graph Transformer with Adaptive Node Sampling", NeurIPS 2022
J.H.Lee, Review on "Graph Pointer Neural Networks", AAAI 2022
H.B.Kim, Review on "subgraph2vec: Learning Distributed Representations of Rooted Sub-graphs from Large Graphs", arXiv preprint, arXiv:2205.10852, 2016
Jun 26th, 2023
V.T.Hoang, Review on "Representation Learning On Heterogeneous Information Networks with Graph Transformer", WWW 2023
J.H.Lee, Review on "CoRGi: Content-Rich Graph Neural Networks with Attention", KDD 2022
H.B.Kim, Review on "Asymmetric transitivity preserving graph embedding", KDD 2016
Jun 22nd, 2023
V.T.Hoang, Review on "Are Graph Attention Networks Attentive Enough? Rethinking Graph Attention by Capturing Homophily and Heterophily", ICLR 2023
J.H.Lee, Review on "Self-supervised Heterogeneous Graph Pre-training Based on Structural Clustering", NeurIPS 2022
J.H.Lee, Review on "Self-Supervised Graph Neural Networks via Diverse and Interactive Message Passing", AAAI 2022
Jun 15th, 2023
V.T.Hoang, Review on "Long Range Graph Benchmark.", NeurIPS 2022
S.T.Nguyen, Review on "Continuous-Time Sequential Recommendation with Temporal Graph Collaborative Transformer", CIKM 2021
Jun 8th, 2023
V.T.Hoang, Review on "Structure-Aware Transformer for Graph Representation Learning", ICML 2022
S.T.Nguyen, Review on "DropAGG: Robust Graph Neural Networks via Drop Aggregation", Neural Networks, 2023
Jun 1st, 2023
V.T.Hoang, Review on "Graph Clustering with Graph Neural Networks", Journal of Machine Learning Research, 2023
S.T.Nguyen, Review on "Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting", IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020
J.H.Lee, Review on "GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training", KDD 2020
May 25th, 2023
V.T.Hoang, Review on "Structured self-attention architecture for graph-level representation learning", Pattern Recognition, 2020
S.T.Nguyen, Review on "Hypergraph Neural Networks", AAAI 2019
J.H.Lee, Review on "How Attentive are Graph Attention Networks?", ICLR 2022
May 19th, 2023
V.T.Hoang, Review on "Two-Dimensional Weisfeiler-Lehman Graph Neural Networks for Link Prediction", arXiv preprint, arXiv:2206.09567, 2022
J.H.Lee, Review on "Graph Pre-training for AMR Parsing and Generation", ACL 2022
May 12th, 2023
V.T.Hoang, Review on "Multi-Order-Content-Based Adaptive Graph Attention Network for Graph Node Classification", Symmetry, 2023
S.T.Nguyen, Review on "Do We Really Need Complicated Model Architectures For Temporal Networks?.” ICLR 2023
J.H.Lee, Review on "Rethinking the Expressive Power of GNNs via Graph Biconnectivity", ICLR 2023
May 5th, 2023
V.T.Hoang, Review on "Exploiting Neighbor Effect: Conv-Agnostic GNNs Framework for Graphs with Heterophily", IEEE Transactions on Neural Networks and Learning Systems, 2023
S.T.Nguyen, Review on "Are More Layers Beneficial to Graph Transformers?", ICLR 2023
J.H.Lee, Review on "Rethinking the Expressive Power of GNNs via Graph Biconnectivity", ICLR 2023
Apr 28th, 2023
V.T.Hoang, Review on "Heterogeneous Graph Transformer", WWW 2020
S.T.Nguyen, Review on "Weather-Aware Fiber-Wireless Traffic Prediction Using Graph Convolutional Networks", IEEE Access, 2022
Apr 21st, 2023
V.T.Hoang, Review on "Gophormer: Ego-Graph Transformer for Node Classification", arXiv preprint, arXiv:2110.13094, 2021
S.T.Nguyen, Review on "Make Heterophily Graphs Better Fit GNN: A Graph Rewiring Approach", arXiv preprint, arXiv:2209.08264, 2022
Apr 14th, 2023
V.T.Hoang, Review on "Role Equivalence Attention for Label Propagation in Graph Neural Networks", PAKDD 2020
S.T.Nguyen, Review on "DeepGCNs: Can GCNs Go as Deep as CNNs?", ICCV 2019
J.H.Lee, Review on "Recipe for a General, Powerful, Scalable Graph Transformer", NeurIPS 2022
Apr 7th, 2023
V.T.Hoang, Review on "Principal Neighbourhood Aggregation for Graph Nets", NeurIPS 2020
S.T.Nguyen, Review on "Hierarchical Graph Convolutional Networks for Semi-supervised Node Classification", IJCAI 2019
J.H.Lee, Review on "MGCN: Semi-supervised Classification in Multi-layer Graphs with Graph Convolutional Networks", ASONAM 2019
Mar 31st, 2023
V.T.Hoang, Review on "Relative Molecule Self-Attention Transformer", ICLR 2022
S.T.Nguyen, Review on "Multi-modal Trajectory Prediction for Autonomous Driving with Semantic Map and Dynamic Graph Attention Network", NeurIPS 2020
J.H. Lee, Review on "Abstract Meaning Representation for Sembanking", ACL 2013
Mar 24th, 2023
V.T. Hoang, Review on "Graph Neural Networks with Learnable Structural and Positional Representations", ICLR 2022
S.T. Nguyen, Review on "Geom-GCN: Geometric Graph Convolutional Networks", ICLR 2020
J.H. Lee, Review on "Learning Intents behind Interactions with Knowledge Graph for Recommendation", WWW 2021
Mar 17th, 2023
V.T. Hoang, Review on "DropEdge: Towards Deep Graph Convolutional Networks on Node Classification", ICLR 2020
S.T. Nguyen, Review on "Graph Pointer Neural Networks", AAAI 2022
J.H. Lee, Review on "Scaling Law for Recommendation Models: Towards General-purpose User Representations", AAAI 2023
Mar 6th, 2023
V.T. Hoang, Review on "Everything is Connected: Graph Neural Networks", Current Opinion in Structural Biology, 2023
S.T. Nguyen, Review on "DeeperGCN: All You Need to Train Deeper GCNs", arXiv preprint, arXiv:2006.07739, 2020
J.H. Lee, "Hyperbolic graph convolutional neural networks", NeurIPS 2019
Feb 27th, 2023
V.T. Hoang, Review on "Graph Neural Networks Go Forward-Forward", arXiv preprint, arXiv:2302.05282, 2023
S.T. Nguyen, Review on "On Generalized Degree Fairness in Graph Neural Networks", AAAI 2023
Feb 20th, 2023
S.T. Nguyen, Review on "Deep Graph Neural Network (part 2)"
Y.J. Yang, Review on "Supervised Learning"
H.B. Kim, Review on "Convoluitional Network"
Feb 11th, 2023
S.T. Nguyen, Review on "Deep Graph Neural Network"
H.B. Kim, Review on "Overall of Neural Network Optimization"
Feb 9th, 2023
S.T. Nguyen, Review on "Link Analysis: HITS and PageRank"
Jan 2nd, 2023
S.T. Nguyen, Review on "Power-laws and Preferential attachment"
