Multiple Types Of Edges Research Articles

Meta-path-based key node identification in heterogeneous networks

Identifying key nodes in complex networks remains challenging. Whereas previous studies focused on homogeneous networks, real-world systems comprise multiple node and edge types. We propose a meta-path-based key node identification (MKNI) method in heterogeneous networks to better capture complex interconnectivity. Considering that existing studies ignore the differences in propagation probabilities between nodes, MKNI leverages meta-paths to extract semantics and perform node embeddings. Trust probabilities reflecting propagation likelihoods are derived by calculating embedding similarities. Node importance is calculated by using metrics incorporating direct and indirect influence based on trust. The experimental results on three real-world network datasets, DBLP, ACM and Yelp, show that the key nodes identified by MKNI exhibit better information propagation in the Susceptible Infected (SI) and susceptibility-influence model (SIR) model compared to other methods. The proposed method provides a reliable tool for revealing the topological structure and functional mechanisms of the network, which can guide more effective regulation and utilization of the network.

Enabling Graph Neural Networks for Semi-Supervised Risk Prediction in Online Credit Loan Services

Graph neural networks (GNNs) are playing exciting roles in the application scenarios where features are hidden in information associations. Fraud prediction of online credit loan services (OCLSs) is such a typical scenario. But it has another rather critical challenge, i.e., the scarcity of data labels. Fortunately, GNNs can also cope with this problem due to their good ability of semi-supervised learning by mining structure and feature information within graphs. Nevertheless, the gain of internal information is often too limited to help GNNs handle the extreme deficiency of labels with high performance beyond the basic requirement of fraud prediction in OCLSs. Therefore, adding labels from the experts, such as manually adding labels through rules, has become a logical practice. However, the existing rule engines for OCLSs have the confliction problem among continuously accumulated rules. To address this issue, we propose a Snorkel-based Semi-Supervised GNN (S3GNN). Under S3GNN, we specially design an upgraded version of the rule engines, called Graph-Oriented Snorkel (GOS), a graph-specific extension of Snorkel, a widely used weakly supervised learning framework, to design rules by subject matter experts (SMEs) and resolve confliction. In particular, in the graph of an anti-fraud scenario, each node pair may have multiple different types of edges, so we propose the Multiple Edge-Types Based Attention mechanism. In general, for the heterogeneous information and multiple relations in the graph, we first obtain the embedding of applicant nodes by aggregating the representation of attribute nodes, and then use the attention mechanism to aggregate neighbor nodes on multiple meta-paths to get ultimate applicant node embedding. We conduct experiments over the real-life data of a large financial platform. The results demonstrate that S3GNN can outperform the state-of-the-art methods, including the method of pilot platform.

Inductive Meta-path Learning for Schema-complex Heterogeneous Information Networks.

Heterogeneous Information Networks (HINs) are information networks with multiple types of nodes and edges. The concept of meta-path, i.e., a sequence of entity types and relation types connecting two entities, is proposed to provide the meta-level explainable semantics for various HIN tasks. Traditionally, meta-paths are primarily used for schema-simple HINs, e.g., bibliographic networks with only a few entity types, where meta-paths are often enumerated with domain knowledge. However, the adoption of meta-paths for schema-complex HINs, such as knowledge bases (KBs) with hundreds of entity and relation types, has been limited due to the computational complexity associated with meta-path enumeration. Additionally, effectively assessing meta-paths requires enumerating relevant path instances, which adds further complexity to the meta-path learning process. To address these challenges, we propose SchemaWalk, an inductive meta-path learning framework for schema-complex HINs. We represent meta-paths with schema-level representations to support the learning of the scores of meta-paths for varying relations, mitigating the need of exhaustive path instance enumeration for each relation. Further, we design a reinforcement-learning based path-finding agent, which directly navigates the network schema (i.e., schema graph) to learn policies for establishing meta-paths with high coverage and confidence for multiple relations. Extensive experiments on real data sets demonstrate the effectiveness of our proposed paradigm.

KPI-HGNN: Key provenance identification based on a heterogeneous graph neural network for big data access control

Dynamic access control of big data has received much attention in recent years because of the characteristics of dynamic generation and multi-source aggregation of big data resources. Provenance-based access control defines dependency paths between resource states through provenance data, and it can dynamically realize access control according to different stages and states of resources. However, big data systems involve huge resources, and the state transition process is complex. The existing research on provenance-based access control techniques faces problems such as low efficiency of manually formulating dependency paths, redundant information in provenance data, and uneven distribution of resources. To solve these problems, this paper proposes a key provenance identification framework KPI-HGNN based on the heterogeneous graph neural network. In the framework, a community detection algorithm based on the heterogeneous graph neural network is designed to realize automatic identification and division of corresponding regions of big data resources through feature fusion of multiple types of nodes and edges. Meanwhile, a key node identification algorithm based on the heterogeneous graph attention network is designed to identify the key node in each community by weighted aggregation of the neighboring node attention coefficients. Besides, a key dependency path discovery algorithm is designed, and access control rules are automatically generated based on key dependency paths. The experimental results indicate that the community detection clustering index of the proposed method is better, the Top-5% key node identification accuracy is higher, and the resource coverage rate and the average percentage number product of key nodes of the provenance-based access control rules are better than those of the baseline method. These results indicate that the proposed key provenance identification framework can efficiently solve the problems in identifying key provenance information faced by dynamic access control of big data.

MECCH: Metapath Context Convolution-based Heterogeneous Graph Neural Networks

Heterogeneous graph neural networks (HGNNs) were proposed for representation learning on structural data with multiple types of nodes and edges. To deal with the performance degradation issue when HGNNs become deep, researchers combine metapaths into HGNNs to associate nodes closely related in semantics but far apart in the graph. However, existing metapath-based models suffer from either information loss or high computation costs. To address these problems, we present a novel Metapath Context Convolution-based Heterogeneous Graph Neural Network (MECCH). MECCH leverages metapath contexts, a new kind of graph structure that facilitates lossless node information aggregation while avoiding any redundancy. Specifically, MECCH applies three novel components after feature preprocessing to extract comprehensive information from the input graph efficiently: (1) metapath context construction, (2) metapath context encoder, and (3) convolutional metapath fusion. Experiments on five real-world heterogeneous graph datasets for node classification and link prediction show that MECCH achieves superior prediction accuracy compared with state-of-the-art baselines with improved computational efficiency. The code is available at https://github.com/cynricfu/MECCH.

Interaction-Aware Graph Neural Networks for Fault Diagnosis of Complex Industrial Processes.

Fault diagnosis of complex industrial processes becomes a challenging task due to various fault patterns in sensor signals and complex interactions between different units. However, how to explore the interactions and integrate with sensor signals remains an open question. Considering that the sensor signals and their interactions in an industrial process with the form of nodes and edges can be represented as a graph, this article proposes a novel interaction-aware and data fusion method for fault diagnosis of complex industrial processes, named interaction-aware graph neural networks (IAGNNs). First, to describe the complex interactions in an industrial process, the sensor signals are transformed into a heterogeneous graph with multiple edge types, and the edge weights are learned by the attention mechanism, adaptively. Then, multiple independent graph neural network (GNN) blocks are employed to extract the fault feature for each subgraph with one edge type. Finally, each subgraph feature is concatenated or fused by a weighted summation function to generate the final graph embedding. Therefore, the proposed method can learn multiple interactions between sensor signals and extract the fault feature from each subgraph by message passing operation of GNNs. The final fault feature contains the information from raw data and implicit interactions between sensor signals. The experimental results on the three-phase flow facility and power system (PS) demonstrate the reliable and superior performance of the proposed method for fault diagnosis of complex industrial processes.

HGNAS++: Efficient Architecture Search for Heterogeneous Graph Neural Networks

Heterogeneous graphs are commonly used to describe networked data with multiple types of nodes and edges. Heterogeneous Graph Neural Networks (HGNNs) are powerful tools for analyzing heterogeneous graphs. However, designing neural architectures of HGNNs requires extensive domain knowledge and time-consuming manual work. Recently, neural architecture search algorithms have become popular in automatically designing neural architectures for homogeneous graph neural networks. In this paper, we present a Heterogeneous Graph Neural Architecture Search algorithm (HGNAS for short) which allows the automatic design of heterogeneous graph neural architectures. Specifically, HGNAS first designs a new search space based on existing popular HGNNs. Then, HGNAS uses a policy network as the controller to sample and find the best neural architecture from the designed search space by maximizing the expected accuracy of the selected architectures on a given validation dataset. Moreover, we design a new method HGNAS++ to improve the efficiency of HGNAS by training the RNN controller within a generative adversarial learning framework. The basic idea of HGNAS++ is to embed a pairwise ranker into the reinforcement learning based architecture search algorithm. The pairwise ranker can be taken as a discriminator which selects more accurate architectures between pairs of candidate architectures. Then, the RNN controller can be updated more efficiently by only using a relatively small number of candidate architectures selected by the pairwise ranker. Experiments on real-world heterogeneous graph datasets show that HGNAS is capable of designing novel HGNNs that beat the best human-invented HGNNs. On the benchmark datasets, HGNAS++ improves HGNAS in terms of evaluation cost, with a reduction of 50% of the evaluated candidate architectures and a decrease of 24% in search time on average. As a byproduct, HGNAS++ can find sparse yet powerful neural architectures for HGNNs.

Dynamic heterogeneous graph representation learning with neighborhood type modeling

Graph representation learning aims to learn the representations of graph structured data in low-dimensional space, and has a wide range of applications in graph analysis tasks. Real-world networks are generally heterogeneous and dynamic, which contain multiple types of nodes and edges, and the graph may evolve at a high speed over time. The complex heterogeneous properties and rapidly evolving graph structures make it difficult to learn high-quality graph representations for dynamic heterogeneous graphs. Currently, studies concentrated on representation learning of temporal heterogeneous networks are insufficient. Existing methods either rely on meta-paths where the embedding quality heavily depending on experts’ selection, or use network snapshots where the fine-grained temporal information cannot be captured. In this paper, we propose a novel graph neural network model–node signature based Temporal Heterogeneous Graph Attention Network, termed as THGAT, for learning the representations of dynamic heterogeneous networks. THGAT improves the aggregation way of neighborhood information, and pays attention to the enlightenment of the importance of neighbor nodes by heterogeneous information and temporal information that cannot be ignored in the network. We also innovatively propose three node signature methods for encoding the heterogeneous information of the nodes and use the time encoding technique suitable for real-time networks to directly represent the temporal information, so as to overcome the limitations of existing methods. We conduct experiments on four real-world datasets, and the results demonstrate that THGAT improves the representation learning quality significantly, in aspects of link prediction, node classification, and node clustering, compared to the state-of-the-art methods. To make the work more complete, we also analyze the applicable scenarios of the three node signature methods through experiments, respectively.

HINChip: Heterogeneous Information Network Representation with Community Hierarchy Preserving

Heterogeneous information network (HIN) representation aims to learn low-dimensional representations for multiple types of nodes and edges while preserving rich structural and semantic information. Existing HIN embedding methods mainly utilize meta-path, meta-graph, and network schema to guide the learned representations. However, these methods may ignore the community hierarchy information in the information network, which is commonly available in many applications. In this paper, we propose HINChip, a new approach for learning representations of Heterogeneous Information Networks with Community Hierarchy Preserving, including intra-community structures and inter-community relationships. Specifically, multiple homogeneous sub-networks are first constructed based on multiple semantics. Second, hierarchical networks are built from fine to coarse on each homogeneous sub-network, preserving intra-community structures and inter-community relationships on the corresponding semantics. The nodes learn the corresponding vectors from each level of the hierarchical networks by the existing representation learning method. Third, multi-level-integrated semantic attention is proposed to combine the node vectors of each level and weigh the importance of different homogeneous sub-networks under given semantics. The proposed HINChip considers only the topology of HIN and outperforms methods trained with node attributes and labels in the experiments conducted on four real-world datasets.

Learning Heterogeneous Graph Embedding with Metapath-Based Aggregation for Link Prediction

Along with the growth of graph neural networks (GNNs), many researchers have adopted metapath-based GNNs to handle complex heterogeneous graph embedding. The conventional definition of a metapath only distinguishes whether there is a connection between nodes in the network schema, where the type of edge is ignored. This leads to inaccurate node representation and subsequently results in suboptimal prediction performance. In heterogeneous graphs, a node can be connected by multiple types of edges. In fact, each type of edge represents one kind of scene. The intuition is that if the embedding of nodes is trained under different scenes, the complete representation of nodes can be obtained by organically combining them. In this paper, we propose a novel definition of a metapath whereby the edge type, i.e., the relation between nodes, is integrated into it. A heterogeneous graph can be considered as the compound of multiple relation subgraphs from the view of a novel metapath. In different subgraphs, the embeddings of a node are separately trained by encoding and aggregating the neighbors of the intrapaths, which are the instance levels of a novel metapath. Then, the final embedding of the node is obtained by the use of the attention mechanism which aggregates nodes from the interpaths, which is the semantic level of the novel metapaths. Link prediction is a downstream task by which to evaluate the effectiveness of the learned embeddings. We conduct extensive experiments on three real-world heterogeneous graph datasets for link prediction. The empirical results show that the proposed model outperforms the state-of-the-art baselines; in particular, when comparing it to the best baseline, the F1 metric is increased by 10.35% over an Alibaba dataset.

Network models in bioinformatics: modeling and analysis for complex diseases.

Networks are present in different aspects of our life: communication networks, World Wide Web, Social Networks, and can be used to conveniently describe biological and clinical data, such as the interactions of proteins in an organism or the connections of neurons in the brain. Therefore, network science, focusing on the network representations of physical, biological and social phenomena and leading to predictive models of these phenomena, currently represents a vast field of application and research for many scientific and social disciplines. The mathematical background for the study and analysis of networks has its roots in the theory of graphs that allows studying real phenomena in a quantitative way. According to the formalism coming from graph theory, nodes of the graph represent entities, whereas edges represent the associations among them. Currently, in bioinformatics and systems biology, there is a growing interest in analyzing associations among biological molecules at a network level. Since the study of associations in a system-level scale has shown great potential, the use of networks has become the de facto standard for representing such associations, and its application fields span from molecular biology to brain connectome analysis [1]. Molecules of different types, e.g. genes, proteins, ribonucleic acids and metabolites, have fundamental roles in the mechanisms of the cellular processes. The study of their structure and interactions is crucial for different reasons, comprising the development of new drugs and the discovery of disease pathways. Thus, the modeling of the complete set of interactions and associations among biological molecules as a graph is convenient for a variety of reasons. Networks provide a simple and intuitive representation of heterogeneous and complex biological processes. Moreover, they facilitate modeling and understanding of complicated molecular mechanisms combining graph theory, machine learning and deep learning techniques. While proteomics and genomics data, represented as data streams or data tables, are mainly used to screen large populations in case–control studies (e.g. for early detection of diseases), interactomics data are represented as graphs and they add a new dimension of analysis, allowing, for instance, the graph-based comparison of organism’s properties. In general, complex biological systems represented as networks provide an integrated way to look into the dynamic behavior of the cellular system through the interactions of components. For instance, biological networks also referred to as Protein–Protein Interaction Networks, model biochemical interactions among proteins. Nodes represent the proteins from a given organism, and the edges represent the protein–protein interactions [2]. Also, gene regulatory network (GRN) is a collection of genes in a cell, which interact each other and with other substances in the cell, such as proteins or metabolites, thereby governing the rates at which genes in the network are transcribed into mRNA. Similarly, the graph-based modeling of the whole system of the brain elements and their relations, so-called brain connectome, is based on the representation of the regions of interest as nodes, and the representation of functional or anatomical connections as edges [3]. Furthermore, recent discoveries in biology have elucidated that the interplay of molecules of different types (e.g. genes, proteins and ribonucleic acids) is a constitutive block of mechanisms inside cells. Consequently, models describing the interplay should be able to consider the presence of multiple different agents and associations, i.e. multiple different types of nodes and edges, that yield to the so-called heterogeneous networks [4].

Community Discovery Algorithm Based on Multi-Relationship Embedding

Complex systems in the real world often can be modeled as network structures, and community discovery algorithms for complex networks enable researchers to understand the internal structure and implicit information of networks. Existing community discovery algorithms are usually designed for single-layer networks or single-interaction relationships and do not consider the attribute information of nodes. However, many real-world networks consist of multiple types of nodes and edges, and there may be rich semantic information on nodes and edges. The methods for single-layer networks cannot effectively tackle multi-layer information, multi-relationship information, and attribute information. This paper proposes a community discovery algorithm based on multi-relationship embedding. The proposed algorithm first models the nodes in the network to obtain the embedding matrix for each node relationship type and generates the node embedding matrix for each specific relationship type in the network by node encoder. The node embedding matrix is provided as input for aggregating the node embedding matrix of each specific relationship type using a Graph Convolutional Network (GCN) to obtain the final node embedding matrix. This strategy allows capturing of rich structural and attributes information in multi-relational networks. Experiments were conducted on different datasets with baselines, and the results show that the proposed algorithm obtains significant performance improvement in community discovery, node clustering, and similarity search tasks, and compared to the baseline with the best performance, the proposed algorithm achieves an average improvement of 3.1% on Macro-F1 and 4.7% on Micro-F1, which proves the effectiveness of the proposed algorithm.

Demand and influencing factors of Ice-Snow sports tourism products using heterogeneous network

<abstract> <p>Heterogeneous networks are complex directed graphs that incorporate multiple types of vertices and edges, enabling the representation of diverse structural and semantic information, and facilitating the abstraction of real-world phenomena. In the context of modern-day societal demands and pressures, individuals seek emotional solace and physical well-being. Notably, China has witnessed a surge in winter sports tourism, with outdoor activities such as skiing gaining increased attention. However, research in this field is sporadic and lacks a comprehensive analysis of the distinctive features of snow and ice sports tourism. Hence, this paper proposes to investigate the demand for ice and snow sports tourism products and its underlying factors using a heterogeneous network model. Drawing upon relevant theories and content, the study analyzes the primary drivers of the development of ice and snow sports tourism. Furthermore, based on the evaluation of ice and snow sports attractions across five major cities, the research synthesizes various product requirements that affect both existing and potential consumers of ice and snow sports tourism. The ultimate aim is to provide practical insights to guide the development and design of ice and snow sports tourism products and ensure the sustainable development of tourism destinations. The final experimental results reveal that the three most crucial aspects of ice and snow sports tourism products are entertainment, stimulation, and safety, with respective effective rates of 64.0%, 62.1%, and 58.9%.</p> </abstract>

Expanding a database-derived biomedical knowledge graph via multi-relation extraction from biomedical abstracts

BackgroundKnowledge graphs support biomedical research efforts by providing contextual information for biomedical entities, constructing networks, and supporting the interpretation of high-throughput analyses. These databases are populated via manual curation, which is challenging to scale with an exponentially rising publication rate. Data programming is a paradigm that circumvents this arduous manual process by combining databases with simple rules and heuristics written as label functions, which are programs designed to annotate textual data automatically. Unfortunately, writing a useful label function requires substantial error analysis and is a nontrivial task that takes multiple days per function. This bottleneck makes populating a knowledge graph with multiple nodes and edge types practically infeasible. Thus, we sought to accelerate the label function creation process by evaluating how label functions can be re-used across multiple edge types.ResultsWe obtained entity-tagged abstracts and subsetted these entities to only contain compounds, genes, and disease mentions. We extracted sentences containing co-mentions of certain biomedical entities contained in a previously described knowledge graph, Hetionet v1. We trained a baseline model that used database-only label functions and then used a sampling approach to measure how well adding edge-specific or edge-mismatch label function combinations improved over our baseline. Next, we trained a discriminator model to detect sentences that indicated a biomedical relationship and then estimated the number of edge types that could be recalled and added to Hetionet v1. We found that adding edge-mismatch label functions rarely improved relationship extraction, while control edge-specific label functions did. There were two exceptions to this trend, Compound-binds-Gene and Gene-interacts-Gene, which both indicated physical relationships and showed signs of transferability. Across the scenarios tested, discriminative model performance strongly depends on generated annotations. Using the best discriminative model for each edge type, we recalled close to 30% of established edges within Hetionet v1.ConclusionsOur results show that this framework can incorporate novel edges into our source knowledge graph. However, results with label function transfer were mixed. Only label functions describing very similar edge types supported improved performance when transferred. We expect that the continued development of this strategy may provide essential building blocks to populating biomedical knowledge graphs with discoveries, ensuring that these resources include cutting-edge results.

A Heterogeneous Graph Model for Repurposing Drugs against Echinococcosis

Through this research work, we aimed to use a graph model to determine important drug substances that are proper for the Echinococcosis hydatid disease. Also, that model can be used in convergence with other similar diseases due to the genes of the Echinococcus granulosus that causes this parasitic disease, and to analyze it by applying graph algorithms to the established model. Furthermore, there is an urgent need to create a basic model for machine learning and artificial intelligence methods in health and pharmaceuticals. Also, the collection of genes and proteins registered in the official internationally recognized gene-disease databases, as well as the data of drugs and pharmaceutical products used for the specific disease, is the first step in the understanding and development of interdisciplinary science. In this paper, we define a heterogeneous graph with multiple types of nodes and edges as a basic model for future studies, as well as the methods and algorithms used in the study, considering the interrelationship between Echinococcus granulosus genes, proteins, diseases, and drugs.

Collaborative representation learning for nodes and relations via heterogeneous graph neural network

Heterogeneous graphs, which consist of multiple types of nodes and edges, are highly suitable for characterizing real-world complex systems. In recent years, due to their strong capability of capturing rich semantics, heterogeneous graph neural networks (HGNNs) have proven to be a powerful technique for representation learning on heterogeneous graphs. However, most of the existing HGNNs only focus on learning node representations and ignore the learning of relation representations, which are complementary to node representations. To address this limitation, we propose a new HGNN model with Collaborative Representation Learning for Nodes and Relations (named CoNR) for link prediction task in this paper. Collaborative learning means that node representations and relation representations participate in and affect each other’s learning process. Specifically, node representations are obtained through a delicate two-step attention mechanism incorporating relation representations that can hierarchically aggregate information within one relation and across different relations. For relation representations, a relation encoder based on node information is designed to encode node representations into relation representations. Therefore, in this framework, node representations and relation representations are mutually updated in a layer-wise manner and work together to facilitate the downstream tasks better. Extensive experimental results on different datasets show the excellent performance of the proposed CoNR.

Heterogeneous graph neural network for attribute completion

Heterogeneous graphs consist of multiple types of nodes and edges, and contain comprehensive information and rich semantics, which can properly model real-world complex systems. However, the attribute values of nodes are often incomplete with many missing attributes, as the cost of collecting node attributes is prohibitively expensive or even impossible (e.g., sensitive personal information). While a handful of graph neural network (GNN) models are developed for attribute completion in heterogeneous networks, most of them either ignore the use of similarity between nodes in feature space, or overlook the different importance of different-order neighbor nodes for attribute completion, resulting in poor performance. In this paper, we propose a general Attribute Completion framework for HEterogeneous Networks (AC-HEN), which is composed of feature aggregation, structure aggregation, and multi-view embedding fusion modules. Specifically, AC-HEN leverages feature aggregation and structure aggregation to obtain multi-view embeddings considering neighbor aggregation in both feature space and network structural space, which distinguishes different contributions of different neighbor nodes by conducting weighted aggregation. Then AC-HEN uses the multi-view embeddings to complete the missing attributes via an embedding fusion module in a weak supervised learning paradigm. Extensive experiments on three real-world heterogeneous network datasets demonstrate the superiority of AC-HEN against state-of-the-art baselines in both attribute completion and node classification. The source code is available at: https://github.com/Code-husky/AC-HEN.

Consensus embedding for multiple networks: Computation and applications

AbstractMachine learning applications on large-scale network-structured data commonly encode network information in the form of node embeddings. Network embedding algorithms map the nodes into a low-dimensional space such that the nodes that are “similar” with respect to network topology are also close to each other in the embedding space. Real-world networks often have multiple versions or can be “multiplex” with multiple types of edges with different semantics. For such networks, computation ofConsensus Embeddings based on the node embeddings of individual versions can be useful for various reasons, including privacy, efficiency, and effectiveness of analyses. Here, we systematically investigate the performance of three dimensionality reduction methods in computing consensus embeddings on networks with multiple versions: singular value decomposition, variational auto-encoders, and canonical correlation analysis (CCA). Our results show that (i) CCA outperforms other dimensionality reduction methods in computing concensus embeddings, (ii) in the context of link prediction, consensus embeddings can be used to make predictions with accuracy close to that provided by embeddings of integrated networks, and (iii) consensus embeddings can be used to improve the efficiency of combinatorial link prediction queries on multiple networks by multiple orders of magnitude.

Nonnegative matrix tri-factorization based clustering in a heterogeneous information network with star network schema

Heterogeneous Information Networks (HINs) contain multiple types of nodes and edges; therefore, they can preserve the semantic information and structure information. Cluster analysis using an HIN has obvious advantages over a transformation into a homogenous information network, which can promote the clustering results of different types of nodes. In our study, we applied a Nonnegative Matrix Tri-Factorization (NMTF) in a cluster analysis of multiple metapaths in HIN. Unlike the parameter estimation method of the probability distribution in previous studies, NMTF can obtain several dependent latent variables simultaneously, and each latent variable in NMTF is associated with the cluster of the corresponding node in the HIN. The method is suited to co-clustering leveraging multiple metapaths in HIN, because NMTF is employed for multiple nonnegative matrix factorizations simultaneously in our study. Experimental results on the real dataset show that the validity and correctness of our method, and the clustering result are better than that of the existing similar clustering algorithm.

Heterogeneous relational message passing networks for molecular dynamics simulations

With many frameworks based on message passing neural networks proposed to predict molecular and bulk properties, machine learning methods have tremendously shifted the paradigms of computational sciences underpinning physics, material science, chemistry, and biology. While existing machine learning models have yielded superior performances in many occasions, most of them model and process molecular systems in terms of homogeneous graph, which severely limits the expressive power for representing diverse interactions. In practice, graph data with multiple node and edge types is ubiquitous and more appropriate for molecular systems. Thus, we propose the heterogeneous relational message passing network (HermNet), an end-to-end heterogeneous graph neural networks, to efficiently express multiple interactions in a single model with ab initio accuracy. HermNet performs impressively against many top-performing models on both molecular and extended systems. Specifically, HermNet outperforms other tested models in nearly 75%, 83% and 69% of tasks on revised Molecular Dynamics 17 (rMD17), Quantum Machines 9 (QM9) and extended systems datasets, respectively. In addition, molecular dynamics simulations and material property calculations are performed with HermNet to demonstrate its performance. Finally, we elucidate how the design of HermNet is compatible with quantum mechanics from the perspective of the density functional theory. Besides, HermNet is a universal framework, whose sub-networks could be replaced by other advanced models.