Query: neighbor Graph Neural Network

title: Bilinear Graph Neural Network with Neighbor Interactions
abstract: Graph Neural Network (GNN) is a powerful model to learn representations and make predictions on graph data. Existing efforts on GNN have largely defined the graph convolution as a weighted sum of the features of the connected nodes to form the representation of the target node. Nevertheless, the operation of weighted sum assumes the neighbor nodes are independent of each other, and ignores the possible interactions between them. When such interactions exist, such as the co occurrence of two neighbor nodes is a strong signal of the target node's characteristics, existing GNN models may fail to capture the signal. In this work, we argue the importance of modeling the interactions between neighbor nodes in GNN. We propose a new graph convolution operator, which augments the weighted sum with pairwise interactions of the representations of neighbor nodes. We term this framework as Bilinear Graph Neural Network (BGNN) which improves GNN representation ability with bilinear interactions between neighbor nodes. In particular, we specify two BGNN models named BGCN and BGAT, based on the well known GCN and GAT, respectively. Empirical results on three public benchmarks of semi supervised node classification verify the effectiveness of BGNN BGCN (BGAT) outperforms GCN (GAT) by 1.6% (1.5% in classification accuracy.Codes are available at: this https URL.

title: Interpretable Nearest Neighbor Queries for Tree Structured Data in Vector Databases of Graph Neural Network Embeddings
abstract: We propose a method for interpreting similarity computations between neural embeddings of trees. We showcase our approach in a search engine for mathematical equations crawled from arxiv.org. The equations are encoded as MathML, a XML file format that describes math in tree structures. These trees are processed by a graph convolutional neural network to obtain fixedsize low dimensional and dense embedding vectors for each tree. Search is performed by performing nearest neighbor query in the set of embeddings. However just by the embeddings it is difficult to judge how the similarity came about. We propose two different approaches to highlight parts of the equation that are important for the similarity, one based on the forward computation of the neural network and one based on the gradient in the backwardcomputation. In a qualitative study on math retrieval we show the advantages of both methods.

title: MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding
abstract: A large number of real world graphs or networks are inherently heterogeneous, involving a diversity of node types and relation types. Heterogeneous graph embedding is to embed rich structural and semantic information of a heterogeneous graph into low dimensional node representations. Existing models usually define multiple metapaths in a heterogeneous graph to capture the composite relations and guide neighbor selection. However, these models either omit node content features, discard intermediate nodes along the metapath, or only consider one metapath. To address these three limitations, we propose a new model named Metapath Aggregated Graph Neural Network (MAGNN) to boost the final performance. Specifically, MAGNN employs three major components, i.e. the node content transformation to encapsulate input node attributes, the intra metapath aggregation to incorporate intermediate semantic nodes, and the inter metapath aggregation to combine messages from multiple metapaths. Extensive experiments on three real world heterogeneous graph datasets for node classification, node clustering, and link prediction show that MAGNN achieves more accurate prediction results than state of the art baselines.

title: Continuous Time Link Prediction via Temporal Dependent Graph Neural Network
abstract: Recently, graph neural networks (GNNs) have been shown to be an effective tool for learning the node representations of the networks and have achieved good performance on the semi supervised node classification task. However, most existing GNNs methods fail to take networks' temporal information into account, therefore, cannot be well applied to dynamic network applications such as the continuous time link prediction task. To address this problem, we propose a Temporal Dependent Graph Neural Network (TDGNN) a simple yet effective dynamic network representation learning framework which incorporates the network temporal information into GNNs. TDGNN introduces a novel Temporal Aggregator (TDAgg) to aggregate the neighbor nodes' features and edges' temporal information to obtain the target node representations. Specifically, it assigns the neighbor nodes aggregation weights using an exponential distribution to bias different edges' temporal information. The performance of the proposed method has been validated on six real world dynamic network datasets for the continuous time link prediction task. The experimental results show that the proposed method outperforms several state of the art baselines.

title: Rumor detection based on propagation graph neural network with attention mechanism
abstract: Rumors on social media have always been an important issue that seriously endangers social security. Researches on timely and effective detection of rumors have aroused lots of interest in both academia and industry. At present, most existing methods identify rumors based solely on the linguistic information without considering the temporal dynamics and propagation patterns. In this work, we aim to solve rumor detection task under the framework of representation learning. We first propose a novel way to construct the propagation graph by following the propagation structure (who replies to whom) of posts on Twitter. Then we propose a gated graph neural network based algorithm called PGNN, which can generate powerful representations for each node in the propagation graph. The proposed PGNN algorithm repeatedly updates node representations by exchanging information between the neighbor nodes via relation paths within a limited time steps. On this basis, we propose two models, namely GLO PGNN (rumor detection model based on the global embedding with propagation graph neural network) and ENS PGNN (rumor detection model based on the ensemble learning with propagation graph neural network) They respectively adopt different classification strategies for rumor detection task, and further improve the performance by including attention mechanism to dynamically adjust the weight of each node in the propagation graph. Experiments on a real world Twitter dataset demonstrate that our proposed models achieve much better performance than state of the art methods both on the rumor detection task and early detection task.

title: Graph Neural Network for Tag Ranking in Tag enhanced Video Recommendation
abstract: In tag enhanced video recommendation systems, videos are attached with some tags that highlight the contents of videos from different aspects. Tag ranking in such recommendation systems provides personalized tag lists for videos from their tag candidates. A better tag ranking model could attract users to click more tags, enter their corresponding tag channels, and watch more tag specific videos, which improves both tag click rate and video watching time. However, most conventional tag ranking models merely concentrate on tag video relevance or tag related behaviors, ignoring the rich information in video related behaviors. We should consider user preferences on both tags and videos. In this paper, we propose a novel Graph neural network based tag ranking (GraphTR) framework on a huge heterogeneous network with video, tag, user and media. We design a novel graph neural network that combines multi field transformer, GraphSAGE and neural FM layers in node aggregation. We also propose a neighbor similarity based loss to encode various user preferences into heterogeneous node representations. In experiments, we conduct both offline and online evaluations on a real world video recommendation system in WeChat Top Stories. The significant improvements in both video and tag related metrics confirm the effectiveness and robustness in real world tag enhanced video recommendation. Currently, GraphTR has been deployed on WeChat Top Stories for more than six months. The source codes are in https:/github.com/lqfarmer/GraphTR.

title: Understanding Image Retrieval Re Ranking: A Graph Neural Network Perspective
abstract: The re ranking approach leverages high confidence retrieved samples to refine retrieval results, which have been widely adopted as a post processing tool for image retrieval tasks. However, we notice one main flaw of re ranking, i.e. high computational complexity, which leads to an unaffordable time cost for real world applications. In this paper, we revisit re ranking and demonstrate that re ranking can be reformulated as a high parallelism Graph Neural Network (GNN) function. In particular, we divide the conventional re ranking process into two phases, i.e. retrieving high quality gallery samples and updating features. We argue that the first phase equals building the k nearest neighbor graph, while the second phase can be viewed as spreading the message within the graph. In practice, GNN only needs to concern vertices with the connected edges. Since the graph is sparse, we can efficiently update the vertex features. On the Market 1501 dataset, we accelerate the re ranking processing from 89.2s to 9.4ms with one K40m GPU, facilitating the real time post processing. Similarly, we observe that our method achieves comparable or even better retrieval results on the other four image retrieval benchmarks, i.e. VeRi 776, Oxford 5k, Paris 6k and University 1652, with limited time cost. Our code is publicly available.

title: Bilinear Graph Neural Network with Node Interactions
abstract: Graph Neural Network (GNN) is a powerful model to learn representations and make predictions on graph data. Existing efforts on GNN have largely defined the graph convolution as a weighted sum of the features of the connected nodes to form the representation of the target node. Nevertheless, the operation of weighted sum assumes the neighbor nodes are independent of each other, and ignores the possible interactions between them. When such interactions exist, such as the co occurrence of two neighbor nodes is a strong signal of the target node's characteristics, existing GNN models may fail to capture the signal. In this work, we argue the importance of modeling the interactions between neighbor nodes in GNN. We propose a new graph convolution operator, which augments the weighted sum with pairwise interactions of the representations of neighbor nodes. We term this framework as Bilinear Graph Neural Network (BGNN) which improves GNN representation ability with bilinear interactions between neighbor nodes. In particular, we specify two BGNN models named BGCN and BGAT, based on the well known GCN and GAT, respectively. Empirical results on three public benchmarks of semisupervised node classification verify the effectiveness of BGNN BGCN (BGAT) outperforms GCN (GAT) by 1.6% (1.5% in classification accuracy. Codes are available at: https:/github.com/zhuhm1996/bgnn.

title: A Motif Based Graph Neural Network to Reciprocal Recommendation for Online Dating
abstract: Recommender systems have been widely adopted in various large scale Web applications. Among these applications, online dating application has attracted more and more research efforts. Essentially, online dating data is a bipartite graph with sparse reciprocal links. Reciprocal recommendations consider bi directional interests of service and recommended users, not merely the service user's interest. This paper proposes a motif based graph neural network (MotifGNN) for online dating recommendation task. We first define seven kinds of motifs and then design a motif based random walk algorithm to sample neighbor users to learn feature embeddings of each service user. At last, these learned feature embeddings are used to predict whether a reciprocal link exists or not. Experiments are evaluated on two real world online dating datasets. The promising results demonstrate the superiority of the proposed approach against a number of state of the art approaches.

title: A Time Aware Graph Neural Network for Session Based Recommendation
abstract: Recently, Graph Neural Networks (GNNs) have attracted increasing attention in the field of session based recommendation, due to its strong ability on capturing complex interactive transitions within sessions. However, existing GNN based models either lack the use of user's long term historical behaviors or fail to address the impact of collaborative filtering information from neighbor users on the current session, which are both important to boost recommendation. In addition, previous work only focuses on the sequential relations of interactions while neglects the time interval information which can imply the correlations between different interactions. To tackle these problems, we propose a Time Aware Graph Neural Network (TA GNN) for session based recommendation. Specifically, we first construct a user behavior graph by linking the interacted items of the same user according to their corresponding time order. A time aware generator is designed to model the correlations between different nodes of the user behavior graph by considering the time interval information. Moreover, items from the neighbor sessions of the current session are selected to build a neighborhood graph. Then the two graphs are respectively processed by two different modules to learn the representation of the current session, which is applied to produce the final recommendation list. Comprehensive experiments show that our model outperforms state of the art baselines on three real world datastes. We also investigate the performance of TA GNN on different numbers of historical interactions and on different session length, finding that our model presents consistently advantages under different conditions.