Query: heterogeneous GNN

title: Meta GNN: Metagraph Neural Network for Semi supervised learning in Attributed Heterogeneous Information Networks
abstract: Heterogeneous Information Networks (HINs) comprise nodes of different types inter connected through diverse semantic relationships. In many real world applications, nodes in information networks are often associated with additional attributes, resulting in Attributed HINs (or AHINs) In this paper, we study semi supervised learning (SSL) on AHINs to classify nodes based on their structure, node types and attributes, given limited supervision. Recently, Graph Convolutional Networks (GCNs) have achieved impressive results in several graph based SSL tasks. However, they operate on homogeneous networks, while being completely agnostic to the semantics of typed nodes and relationships in real world HINs. In this paper, we seek to bridge the gap between semantic rich HINs and the neighborhood aggregation paradigm of graph neural networks, to generalize GCNs through metagraph semantics. We propose a novel metagraph convolution operation to extract features from local metagraph structured neighborhoods, thus capturing semantic higher order relationships in AHINs. Our proposed neural architecture Meta GNN extracts features of diverse semantics by utilizing multiple metagraphs, and employs a novel metagraph attention module to learn personalized metagraph preferences for each node. Our semi supervised node classification experiments on multiple real world AHIN datasets indicate significant performance gains of 6% Micro F1 on average over state of the art AHIN baselines. Visualizations on metagraph attention weights yield interpretable insights into their relative task specific importance.

title: Heterogeneous Graph Transformer
abstract: Recent years have witnessed the emerging success of graph neural networks (GNNs) for modeling structured data. However, most GNNs are designed for homogeneous graphs, in which all nodes and edges belong to the same types, making it infeasible to represent heterogeneous structures. In this paper, we present the Heterogeneous Graph Transformer (HGT) architecture for modeling Web scale heterogeneous graphs. To model heterogeneity, we design node and edge type dependent parameters to characterize the heterogeneous attention over each edge, empowering HGT to maintain dedicated representations for different types of nodes and edges. To handle Web scale graph data, we design the heterogeneous mini batch graph sampling algorithm HGSampling for efficient and scalable training. Extensive experiments on the Open Academic Graph of 179 million nodes and 2 billion edges show that the proposed HGT model consistently outperforms all the state of the art GNN baselines by 9 21 on various downstream tasks. The dataset and source code of HGT are publicly available at https:/github.com/acbull/pyHGT.

title: Multi hop Reading Comprehension across Multiple Documents by Reasoning over Heterogeneous Graphs
abstract: Multi hop reading comprehension (RC) across documents poses new challenge over single document RC because it requires reasoning over multiple documents to reach the final answer. In this paper, we propose a new model to tackle the multi hop RC problem. We introduce a heterogeneous graph with different types of nodes and edges, which is named as Heterogeneous Document Entity (HDE) graph. The advantage of HDE graph is that it contains different granularity levels of information including candidates, documents and entities in specific document contexts. Our proposed model can do reasoning over the HDE graph with nodes representation initialized with co attention and self attention based context encoders. We employ Graph Neural Networks (GNN) based message passing algorithms to accumulate evidences on the proposed HDE graph. Evaluated on the blind test set of the Qangaroo WikiHop data set, our HDE graph based single model delivers competitive result, and the ensemble model achieves the state of the art performance.

title: Link Inference via Heterogeneous Multi view Graph Neural Networks
abstract: Graph Neural Network (GNN) has been attaching great attention along with its successful industry applications, such as social network, recommendation system and so on. Most existing GNN algorithms for link inference tasks mainly concentrate on homogeneous network where single typed nodes and edges are considered. Besides, they are transductive, incapable of handling unseen data, and are difficult to generalize to big graph data. In this paper, we introduce a new idea, i.e. Heterogeneous Multi view Graph Neural Network (HMGNN) to remedy these problems. A more complex and unstudied heterogeneous network structure where multiple node and edge types co exist, and each of them also contains specific attributes, is learned in this framework. The proposed HMGNN is end to end and two stages are designed: i) The first stage extends the widely used GraphSAGE model to the studied heterogeneous scenario to generate the vector embedded representations for each type of nodes. ii) The second stage develops a novel and inductive subspace based strategy for link inference by aggregating multi typed node and edge feature views. Comprehensive experiments on large scale spam detection and link prediction applications clearly verify the effectiveness of our model.

title: Heterogeneous Molecular Graph Neural Networks for Predicting Molecule Properties
abstract: As they carry great potential for modeling complex interactions, graph neural network (GNN) based methods have been widely used to predict quantum mechanical properties of molecules. Most of the existing methods treat molecules as molecular graphs in which atoms are modeled as nodes. They characterize each atom's chemical environment by modeling its pairwise interactions with other atoms in the molecule. Although these methods achieve a great success, limited amount of works explicitly take many body interactions, i.e. interactions between three and more atoms, into consideration. In this paper, we introduce a novel graph representation of molecules, heterogeneous molecular graph (HMG) in which nodes and edges are of various types, to model many body interactions. HMGs have the potential to carry complex geometric information. To leverage the rich information stored in HMGs for chemical prediction problems, we build heterogeneous molecular graph neural networks (HMGNN) on the basis of a neural message passing scheme. HMGNN incorporates global molecule representations and an attention mechanism into the prediction process. The predictions of HMGNN are invariant to translation and rotation of atom coordinates, and permutation of atom indices. Our model achieves state of the art performance in 9 out of 12 tasks on the QM9 dataset.

title: Heterogeneous Relational Reasoning in Knowledge Graphs with Reinforcement Learning
abstract: Path based relational reasoning over knowledge graphs has become increasingly popular due to a variety of downstream applications such as question answering in dialogue systems, fact prediction, and recommender systems. In recent years, reinforcement learning (RL) has provided solutions that are more interpretable and explainable than other deep learning models. However, these solutions still face several challenges, including large action space for the RL agent and accurate representation of entity neighborhood structure. We address these problems by introducing a type enhanced RL agent that uses the local neighborhood information for efficient path based reasoning over knowledge graphs. Our solution uses graph neural network (GNN) for encoding the neighborhood information and utilizes entity types to prune the action space. Experiments on real world dataset show that our method outperforms state of the art RL methods and discovers more novel paths during the training procedure.

title: Robust Reasoning Over Heterogeneous Textual Information for Fact Verification
abstract: Automatic fact verification (FV) based on artificial intelligence is considered as a promising approach which can be used to identify misinformation distributed on the web. Even though previous FV using deep learning have made great achievements in single dataset (e.g. FEVER) the trained systems are unlikely to be capable of extracting evidence from heterogeneous web sources and validating claims in accordance with evidence found on the Internet. Nevertheless, the heterogeneity covers abundant semantic information, which will help FV system identify misinformation in a more accurate way. The current work is the first attempt to make the combination of knowledge graph (KG) and graph neural network (GNN) to enhance the robustness of FV systems for heterogeneous information. As a result, it can be generalized to multi domain datasets after training on a sufficient single one. To make information update and aggregate well on the collaborative graph, the present study proposes a double graph attention network (DGAT) framework which recursively propagates the embeddings from a node's neighbors to refine the node's embedding as well as applies an attention mechanism to classify the importance of the neighbors. We train and evaluate our system on FEVER, a single and benchmark dataset for FV, and then re evaluate our system on UKP Snopes Corpus, a new richly annotated corpus for FV tasks on the basis of heterogeneous web sources. According to experimental results, although DGAT has no excellent advantages in a single dataset, it shows outstanding performance in more realistic and multi domain datasets. Moreover, the current study also provides a feasible method for deep learning to have the ability to infer heterogeneous information robustly.

title: Exploring Heterogeneous Information Networks via Pre Training
abstract: To explore heterogeneous information networks (HINs) network representation learning (NRL) is proposed, which represents a network in a low dimension space. Recently, graph neural networks (GNNs) have drawn a lot of attention which are very expressive for mining a HIN, while they suffer from low efficiency issue. In this paper, we propose a pre training and fine tuning framework PF HIN to capture the features of a HIN. Unlike traditional GNNs that have to train the whole model for each downstream task, PF HIN only needs to fine tune the model using the pre trained parameters and minimal extra task specific parameters, thus improving the model efficiency and effectiveness. Specifically, in pre training phase, we first use a ranking based BFS strategy to form the input node sequence. Then inspired by BERT, we adopt deep bi directional transformer encoders to train the model, which is a variant of GNN aggregator that is more powerful than traditional deep neural networks like CNN and LSTM. The model is pre trained based on two tasks, i.e. masked node modeling (MNM) and adjacent node prediction (ANP) Additionally, we leverage factorized embedding parameterization and cross layer parameter sharing to reduce the parameters. In fine tuning stage, we choose four benchmark downstream tasks, i.e. link prediction, similarity search, node classification and node clustering. We use node sequence pairs as input for link prediction and similarity search, and a single node sequence as input for node classification and clustering. The experimental results of the above tasks on four real world datasets verify the advancement of PF HIN, as it outperforms state of the art alternatives consistently and significantly.

title: Scalable Graph Neural Networks for Heterogeneous Graphs
abstract: Graph neural networks (GNNs) are a popular class of parametric model for learning over graph structured data. Recent work has argued that GNNs primarily use the graph for feature smoothing, and have shown competitive results on benchmark tasks by simply operating on graph smoothed node features, rather than using end to end learned feature hierarchies that are challenging to scale to large graphs. In this work, we ask whether these results can be extended to heterogeneous graphs, which encode multiple types of relationship between different entities. We propose Neighbor Averaging over Relation Subgraphs (NARS) which trains a classifier on neighbor averaged features for randomly sampled subgraphs of the "metagraph" of relations. We describe optimizations to allow these sets of node features to be computed in a memory efficient way, both at training and inference time. NARS achieves a new state of the art accuracy on several benchmark datasets, outperforming more expensive GNN based methods

title: PanRep: Universal node embeddings for heterogeneous graphs
abstract: Learning unsupervised node embeddings facilitates several downstream tasks such as node classification and link prediction. A node embedding is universal if it is designed to be used by and benefit various downstream tasks. This work introduces PanRep, a graph neural network (GNN) model, for unsupervised learning of universal node representations for heterogenous graphs. PanRep consists of a GNN encoder that obtains node embeddings and four decoders, each capturing different topological and node feature properties. Abiding to these properties the novel unsupervised framework learns universal embeddings applicable to different downstream tasks. PanRep can be furthered fine tuned to account for possible limited labels. In this operational setting PanRep is considered as a pretrained model for extracting node embeddings of heterogenous graph data. PanRep outperforms all unsupervised and certain supervised methods in node classification and link prediction, especially when the labeled data for the supervised methods is small. PanRep FT (with fine tuning) outperforms all other supervised approaches, which corroborates the merits of pretraining models. Finally, we apply PanRep FT for discovering novel drugs for Covid 19. We showcase the advantage of universal embeddings in drug repurposing and identify several drugs used in clinical trials as possible drug candidates.