LC-GNN improves GNNs for node classification by incorporating label consistency.
problem Limited performance of GNNs due to label consistency assumption not always holding.
method LC-GNN uses node pairs with the same label but unconnected to expand GNN's receptive field.
result LC-GNN outperforms traditional GNNs in semi-supervised node classification.
Graph attacks can be successful with just a few bad nodes.
problem Adversarial attacks on graph neural networks.
method Identifying and exploiting anchor nodes to compromise graph models.
result A few bad nodes can significantly degrade graph model performance.
Model shows advantageous position in trade networks leads to success.
problem Characterizing the importance of nodes in trade networks during globalization.
method Mapped trade network evolution to percolation problem and analyzed topological features.
result Advantageous position at different scales determines node success.
Network representation learning (NRL) methods aim to map each vertex into a low dimensional space by preserving the local and global structure of a given network, and in recent years they have received a significant attention thanks to their success in several challenging problems. Although various approaches have been…
GCNs favor high-degree nodes, leading to biased performance; a new method mitigates this.
problem Degree-related biases in GCNs, especially for low-degree nodes.
method Developed a novel SL-DSGC that reduces model and data biases.
result SL-DSGC improves GCN accuracy significantly for low-degree nodes.
Generates missing node attributes for better graph-based tasks.
problem Missing or incomplete node attributes degrade graph-based algorithms' performance.
method Deep adversarial learning-based method (NANG) to generate node attributes.
result Generated node attributes improve node classification and link prediction.
FI-GNNs learn expressive node representations from sparse features.
problem Sparse and high-dimensional node features limit GNN performance.
method Plug-and-play GNN framework that highlights informative feature interactions.
result FI-GNNs learn highly expressive node representations on feature-sparse graphs.
New method uses GNNs and node feature propagation for active learning in graph node classification.
problem Lack of labeled data for graph neural networks.
method Node feature propagation followed by K-Medoids clustering for instance selection.
result Proposed method significantly outperforms other methods on benchmark datasets.
Boost GNNs for node classification by incorporating label dependencies.
problem Current GNNs lack expressiveness and fail to capture label dependencies.
method Proposes a collective learning framework combining collective classification and self-supervised learning.
result Consistent, significant improvement in node classification accuracy across various GNNs.
SIGNNAP learns stable and identifiable node representations in GNNs against graph perturbations.
problem Fragility of GNN models to graph perturbations leading to unreliable node representations.
method SIGNNAP proposes a novel model that learns stable and identifiable node representations in an unsupervised manner, formalizing stability and identifiability through a contrastive objective and preserving smoothness with existing GNN backbones.
result SIGNNAP demonstrates effectiveness in learning stable and identifiable node representations in GNNs against graph perturbations on six benchmarks.
New walk extraction strategies improve node embeddings in KGs.
problem Improving node embeddings in knowledge graphs.
method Proposed five different walk extraction strategies to complement basic random walks.
result The n-gram strategy performs best on average for node classification tasks.
Graph attention auto-encoder reconstructs graph structure and attributes.
problem Lack of methods to reconstruct graph structure and node attributes in graph auto-encoders.
method Stacked encoder/decoder layers with self-attention mechanisms, regularized node representations to reconstruct graph structure.
result Competitive performance on node classification benchmarks, including inductive learning.
Automates graph convolutional network design for semi-supervised node classification.
problem Designing optimal graph convolutional network architectures for semi-supervised node classification.
method An automatic process to define a problem-specific architecture based on graph structure.
result The proposed method outperforms existing methods in classification performance and network compactness.
Proposes a novel node embedding framework for graphs using Fisher Information.
problem Lack of theoretical understanding of attention-based GNNs.
method Uses hierarchical kernels and Fisher Information to learn node embeddings.
result Proposed method outperforms existing GNNs on node classification benchmarks.
The nonparametric problem of detecting existence of an anomalous interval over a one dimensional line network is studied. Nodes corresponding to an anomalous interval (if exists) receive samples generated by a distribution q, which is different from the distribution p that generates samples for other nodes. If anomalou…
Indirect attacks can fool graph classifiers even with poisoned neighbors.
problem How to evaluate and defend graph convolutional neural networks against indirect adversarial attacks.
method Proposed a method to generate adversarial perturbations on a single node far from the target.
result 99% attack success rate within two-hops from the target in two datasets.
The paper improves uncertainty quantification for node classification using distance-based regularization.
problem Uncertainty in deep learning models, especially for node classification tasks.
method Graph posterior networks (GPNs) with UCE loss function, followed by a distance-based regularization.
result The proposed distance-based regularization outperforms state-of-the-art methods in OOD detection and misclassification detection.
Graph-based state representation improves deep RL performance.
problem High sample-complexity and starting with a good input representation in deep RL.
method Exploiting the graph structure of MDPs for effective state representation learning.
result Graph-based node representation methods outperform matrix-based methods in grid-world navigation tasks.
Proposes a novel graph representation learning framework using contrastive methods.
problem Graph representation learning for graph-structured data.
method Leverages a contrastive objective at the node level, generating two graph views by corruption and learning node representations by maximizing agreement.
result Consistently outperforms existing state-of-the-art methods on transductive and inductive learning tasks.
CompGCN embeds nodes and relations in multi-relational graphs.
problem Handling multi-relational graphs with direction and labels.
method CompGCN uses entity-relation composition operations from KG embedding.
result CompGCN achieves superior results on node classification, link prediction, and graph classification.
SLiCE learns contextual node embeddings for link prediction in heterogeneous networks.
problem Link prediction requires specific contextual information not captured by static node embeddings.
method Self-supervised pre-training with localized attention mechanisms.
result SLiCE significantly outperforms existing methods on link prediction tasks.
New BNN model proves optimal posterior concentration and enables practical inference.
problem Improving generalization and uncertainty quantification in deep neural networks.
method Proposes a new node-sparse BNN model with theoretical guarantees and a novel MCMC algorithm for inference.
result Proves near minimax optimal posterior concentration rate and adaptiveness to true model smoothness.
Paper proposes a novel graph AL method using contrastive learning.
problem Discovering informative nodes for GNNs with unlabeled data.
method Integrates graph AL with contrastive learning, focusing on homophilous subgraphs.
result Method outperforms state-of-the-arts on five public datasets.
HGT models heterogeneous graphs with dedicated node and edge representations.
problem Infeasibility of existing GNNs for heterogeneous graphs.
method Node- and edge-type dependent parameters, relative temporal encoding, heterogeneous mini-batch graph sampling.
result HGT outperforms state-of-the-art GNNs by 9%--21% on various downstream tasks.
Graph representation learning resurges as a trending research subject owing to the widespread use of deep learning for Euclidean data, which inspire various creative designs of neural networks in the non-Euclidean domain, particularly graphs. With the success of these graph neural networks (GNN) in the static setting, …
New method detects overlapping communities in weighted graphs without pure nodes assumption.
problem Detect overlapping communities in weighted graphs without making pure nodes assumption.
method Convex optimization-based approach for weighted graphs.
result Success on artificial and real-world datasets.
Graphs (networks) are ubiquitous and allow us to model entities (nodes) and the dependencies (edges) between them. Learning a useful feature representation from graph data lies at the heart and success of many machine learning tasks such as classification, anomaly detection, link prediction, among many others. Many exi…
DEMO-Net improves graph neural networks by focusing on node degree.
problem Limited analysis of graph convolution properties and lack of degree-specific graph structure.
method Proposes DEMO-Net, a degree-specific graph neural network that recursively identifies 1-hop neighborhood structures and uses multi-task learning for node representation learning.
result Demonstrates effectiveness and efficiency of DEMO-Net on node and graph classification benchmarks.
The goal of network representation learning is to learn low-dimensional node embeddings that capture the graph structure and are useful for solving downstream tasks. However, despite the proliferation of such methods, there is currently no study of their robustness to adversarial attacks. We provide the first adversari…
The success of graph embeddings or node representation learning in a variety of downstream tasks, such as node classification, link prediction, and recommendation systems, has led to their popularity in recent years. Representation learning algorithms aim to preserve local and global network structure by identifying no…
LADIES improves GCN training efficiency and accuracy for large graphs.
problem Training large graph convolutional networks (GCNs) is computationally expensive.
method LADIES uses layer-dependent importance sampling to select nodes for training.
result LADIES outperforms previous methods in both time and memory efficiency.
GraphSTONE uses topic models to capture graph structures, improving GCN performance.
problem GCNs focus too much on node features and not enough on graph structures.
method GraphSTONE employs topic models of graphs to capture structural topics, which guide the aggregation of node features.
result GraphSTONE outperforms GCNs in performance, efficiency, and interpretability.
Unified framework for hyperbolic network embedding considering multiplex interactions.
problem Misleading results from ignoring multiplex interactions in real-world networks.
method Combines multiplex network hyperbolic embedding and community detection using random walk.
result Effective and efficient node embedding across multiplex channels compared to state-of-the-art.
Bayesian graph contrastive learning improves uncertainty quantification for graph analytics.
problem Uncertainty quantification for node representations in graph contrastive learning.
method Proposes a Bayesian framework to learn stochastic encoders representing nodes as distributions, providing uncertainty estimates.
result Significant improvement in performance on benchmark datasets compared to existing methods.
CorePPR combines PageRank and CoreRank for scalable GNNs.
problem Expensive message passing in GNNs for large graphs.
method Learnable convex combination of approximate PPR and CoreRank, dynamic node selection.
result CorePPR outperforms PPRGo on large graphs.
This paper proposes a method to learn graph representations by partitioning edges into communities.
problem Graph neural networks ignore how edges are formed, leading to suboptimal representation learning.
method Introduces a generative model to partition edges into community-specific weighted edges, then uses these for GNN-based inference and classification.
result The method learns discriminative representations for both node-level and graph-level classification tasks.
A GPU framework speeds up BnB for discrete optimization problems.
problem Optimizing large-scale discrete problems with GPU limitations.
method Parallel BnB nodes in GPU batches, using padding and custom kernels.
result One to two orders of magnitude speedup and zero optimality gap.
IsoNN learns graph representations without node order constraints.
problem Lack of interpretability and erratic performance in graph classification due to node-orderless property.
method IsoNN uses graph matching with subgraph templates to learn isomorphic features and break node-order.
result IsoNN achieves superior performance on graph classification tasks compared to existing methods.
Visual rendering of graphs is a key task in the mapping of complex network data. Although most graph drawing algorithms emphasize aesthetic appeal, certain applications such as travel-time maps place more importance on visualization of structural network properties. The present paper advocates a graph embedding approac…
This work tackles over-smoothing in GNNs and proposes methods to improve node representation quality.
problem Over-smoothing in GNNs leads to indistinguishable node representations across different classes.
method Developed quantitative metrics (MAD, MADGap) to measure smoothness and over-smoothness. Proposed two methods: MADReg and AdaGraph.
result Proposed methods effectively alleviate over-smoothing and improve GNN performance.
GCL-LRR improves node classification in noisy graphs.
problem Noise in real-world graph data impairs GNNs' effectiveness.
method Two-stage transductive learning with low-rank regularization and attention.
result Improved node classification performance in noisy graphs.
ECGs improve GNNs for non-homophilic data.
problem Improving GNNs for datasets where nodes are not likely to belong to the same class.
method ECGs rewire GNNs' computation graph to connect nodes likely in the same class using weaker classifiers.
result ECGs improve GNN performance on non-homophilic datasets.
Network embeddings have become very popular in learning effective feature representations of networks. Motivated by the recent successes of embeddings in natural language processing, researchers have tried to find network embeddings in order to exploit machine learning algorithms for mining tasks like node classificati…
LATTE tackles heterogeneous network embedding challenges with layer-stacked attention.
problem Aggregating higher-order indirect relations in heterogeneous networks.
method Layer-stacked ATTention Embedding (LATTE) that decomposes meta relations at each layer.
result LATTE achieves state-of-the-art performance on benchmark datasets.
DyHATR learns dynamic heterogeneous networks for better link prediction.
problem Learning effective representations of dynamic heterogeneous networks for link prediction.
method Hierarchical attention for heterogeneous information and temporal RNN for evolutionary patterns.
result DyHATR significantly outperforms state-of-the-art baselines on link prediction tasks.
Proposes MGMN for end-to-end graph similarity learning.
problem Lack of cross-level interactions in graph similarity learning.
method Multi-level graph matching network (MGMN) combining node-graph matching and siamese graph neural networks.
result MGMN outperforms state-of-the-art models on graph-graph classification and regression tasks.
A new method predicts student skill success rates in real-time.
problem Accurate and explainable prediction of student skill success rates in real-time.
method Performance Distribution Tracing (PDT) using a Dynamic Bayesian Network with continuous random variables.
result PDT provides both explainability and accuracy in real-time predictions of student skill success rates.
Many successful methods have been proposed for learning low dimensional representations on large-scale networks, while almost all existing methods are designed in inseparable processes, learning embeddings for entire networks even when only a small proportion of nodes are of interest. This leads to great inconvenience,…