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TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphs
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Abstract
Multi-relational temporal graphs are powerful tools for modeling real-world data, capturing the evolving and interconnected nature of entities over time. Recently, many novel models are proposed for ML on such graphs intensifying the need for robust evaluation and standardized benchmark datasets. However, the availability of such resources remains scarce and evaluation faces added complexity due to reproducibility issues in experimental protocols. To address these challenges, we introduce Temporal Graph Benchmark 2.0 (TGB 2.0), a novel benchmarking framework tailored for evaluating methods for predicting future links on Temporal Knowledge Graphs and Temporal Heterogeneous Graphs with a focus on large-scale datasets, extending the Temporal Graph Benchmark. TGB 2.0 facilitates comprehensive evaluations by presenting eight novel datasets spanning five domains with up to 53 million edges. TGB 2.0 datasets are significantly larger than existing datasets in terms of number of nodes, edges, or timestamps. In addition, TGB 2.0 provides a reproducible and realistic evaluation pipeline for multi-relational temporal graphs. Through extensive experimentation, we observe that 1) leveraging edge-type information is crucial to obtain high performance, 2) simple heuristic baselines are often competitive with more complex methods, 3) most methods fail to run on our largest datasets, highlighting the need for research on more scalable methods.
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Temporal heterogeneous networks (THNs) investigate the structural interactions and their evolution over time in graphs with multiple types of nodes or edges. Existing THNs describe evolving networks as a sequence of graph snapshots and adopt mechanisms from static heterogeneous networks to capture the spatial-temporal correlation. However, these works are confined to the discrete-time setting and the implementation of stacked mechanisms often introduces a high level of complexity, both conceptually and computationally. Here, we conduct comprehensive examinations and propose STHN, a simplifying THN for continuous-time link prediction. Concretely, to integrate continuous dynamics, we maintain a historical interaction memory for each node. A link encoder that incorporates two components - type encoding and relative time encoding - is introduced to encapsulate implicit heterogeneous characteristics of interaction and extract the most informative temporal information. We further propose to use a patching technique that assists with Transformer feature extractor to support the interaction sequence with long histories. Extensive experiments on three real-world datasets empirically demonstrate that STHN outperforms state-of-the-art methods with competitive task accuracy and predictive efficiency on both transductive and inductive settings.
Due to its ability to incorporate and leverage time information in relational data, Temporal Knowledge Graph (TKG) learning has become an increasingly studied research field. With the goal of predicting the future, researchers have presented innovative methods for what is called Temporal Knowledge Graph Forecasting. However, the experimental procedures in this line of work show inconsistencies that strongly influence empirical results and thus lead to distorted comparisons among models. This work focuses on the evaluation of TKG Forecasting models: we describe evaluation settings commonly used in this research area and shed light on its scholarship issues. Further, we provide a unified evaluation protocol and carry out a re-evaluation of state-of-the-art models on the most common datasets under such a setting. Finally, we show the difference in results caused by different evaluation settings. We believe that this work provides a solid foundation for future evaluations of TKG Forecasting models and can thus contribute to the development of this growing research area.
Graph embedding, aiming to learn low-dimensional representations of nodes while preserving valuable structure information, has played a key role in graph analysis and inference. However, most existing methods deal with static homogeneous topologies, while graphs in real-world scenarios are gradually generated with different-typed temporal events, containing abundant semantics and dynamics. Limited work has been done for embedding dynamic heterogeneous graphs since it is very challenging to model the complete formation process of heterogeneous events. In this paper, we propose a novel Heterogeneous Hawkes Process based dynamic Graph Embedding (HPGE) to handle this problem. HPGE effectively integrates the Hawkes process into graph embedding to capture the excitation of various historical events on the current type-wise events. Specifically, HPGE first designs a heterogeneous conditional intensity to model the base rate and temporal influence caused by heterogeneous historical events. Then the heterogeneous evolved attention mechanism is designed to determine the fine-grained excitation to different-typed current events. Besides, we deploy the temporal importance sampling strategy to sample representative events for efficient excitation propagation. Experimental results demonstrate that HPGE consistently outperforms the state-of-the-art alternatives.
Experimental reproducibility and replicability are critical topics in machine learning. Authors have often raised concerns about their lack in scientific publications to improve the quality of the field. Recently, the graph representation learning field has attracted the attention of a wide research community, which resulted in a large stream of works. As such, several Graph Neural Network models have been developed to effectively tackle graph classification. However, experimental procedures often lack rigorousness and are hardly reproducible. Motivated by this, we provide an overview of common practices that should be avoided to fairly compare with the state of the art. To counter this troubling trend, we ran more than 47000 experiments in a controlled and uniform framework to re-evaluate five popular models across nine common benchmarks. Moreover, by comparing GNNs with structure-agnostic baselines we provide convincing evidence that, on some datasets, structural information has not been exploited yet. We believe that this work can contribute to the development of the graph learning field, by providing a much needed grounding for rigorous evaluations of graph classification models.
Analyzing the rich information behind heterogeneous networksthrough network representation learning methods is signifcant for many application tasks such as link prediction, node classifcation and similarity research. As the networks evolve over times, the interactions among the nodes in networks make heterogeneous networks exhibit dynamic characteristics. However, almost all the existing heterogeneous network representation learning methods focus on static networks which ignore dynamic characteristics. In this paper, we propose a novel approach DHNE to learn the representations of nodes in dynamic heterogeneous networks. The key idea of our approach is to construct comprehensive historical-current networks based on subgraphs of snapshots in time step to capture both the historical and current information in the dynamic heterogeneous network. And then under the guidance of meta paths, DHNE performs random walks on the constructed historical-current graphs to capture semantic information. After getting the node sequences through random walks, we propose the dynamic heterogeneous skip-gram model to learn the embeddings. Experiments on large-scale real-world networks demonstrate that the embeddings learned by the proposed DHNE model achieve better performances than state-of-the-art methods in various downstream tasks including node classifcation and visualization.
Modeling sequential interactions between users and items/products is crucial in domains such as e-commerce, social networking, and education. Representation learning presents an attractive opportunity to model the dynamic evolution of users and items, where each user/item can be embedded in a Euclidean space and its evolution can be modeled by an embedding trajectory in this space. However, existing dynamic embedding methods generate embeddings only when users take actions and do not explicitly model the future trajectory of the user/item in the embedding space. Here we propose JODIE, a coupled recurrent neural network model that learns the embedding trajectories of users and items. JODIE employs two recurrent neural networks to update the embedding of a user and an item at every interaction. Crucially, JODIE also models the future embedding trajectory of a user/item. To this end, it introduces a novel projection operator that learns to estimate the embedding of the user at any time in the future. These estimated embeddings are then used to predict future user-item interactions. To make the method scalable, we develop a t-Batch algorithm that creates time-consistent batches and leads to 9x faster training. We conduct six experiments to validate JODIE on two prediction tasks---future interaction prediction and state change prediction---using four real-world datasets. We show that JODIE outperforms six state-of-the-art algorithms in these tasks by at least 20% in predicting future interactions and 12% in state change prediction.
The Conflict and Mediation Events Observations (CAMEO) framework is a new event data coding scheme optimized for the study of third-party mediation in international disputes. We have developed and implemented this system using the TABARI automated coding program and have generated data sets for the Balkans (1989-2002; N=69,620), Levant (1979-2002; N=146,283), and West Africa (1989-2002; N=17,468) from Reuters and Agence France Presse reports. In this paper, we describe why we decided to develop a new coding system, rather than continuing to use the World Events Interaction Survey (WEIS) framework that we have used in earlier work. Our decision involved both known weaknesses in the WEIS system and some additional problems that we have found occur when WEIS is coded using automated methods. We have addressed these problems in constructing CAMEO and have produced much more completed documentation than has been available for WEIS. In the second half of the paper, we make several statistical comparisons of CAMEO-coded and WEIScoded data in the three geographical regions. When the data are aggregated to a general behavioral level---verbal cooperation, material cooperation, verbal conflict and material conflict---most of the data sets show a high correlation (r>0.90) in the number of WEIS and CAMEO events coded per month. However, as we expected, CAMEO consistently picks up a greater number of events involving material cooperation. Finally, there is a very significant correlation (r>0.57) between the count of CAMEO events specifically dealing with mediation and negotiation and a pattern-based measure of mediation we developed earlier from WEIS data. Appendices in the paper show the WEIS and CAMEO coding framework and examples from the CAMEOcodebook.
Temporal Knowledge Graph (TKG) Forecasting aims at predicting links in Knowledge Graphs for future timesteps based on a history of Knowledge Graphs. To this day, standardized evaluation protocols and rigorous comparison across TKG models are available, but the importance of simple baselines is often neglected in the evaluation, which prevents researchers from discerning actual and fictitious progress. We propose to close this gap by designing an intuitive baseline for TKG Forecasting based on predicting recurring facts. Compared to most TKG models, it requires little hyperparameter tuning and no iterative training. Further, it can help to identify failure modes in existing approaches. The empirical findings are quite unexpected: compared to 11 methods on five datasets, our baseline ranks first or third in three of them, painting a radically different picture of the predictive quality of the state of the art.
Recent progress in research on deep graph networks (DGNs) has led to a maturation of the domain of learning on graphs. Despite the growth of this research field, there are still important challenges that are yet unsolved. Specifically, there is an urge of making DGNs suitable for predictive tasks on real-world systems of interconnected entities, which evolve over time. With the aim of fostering research in the domain of dynamic graphs, first, we survey recent advantages in learning both temporal and spatial information, providing a comprehensive overview of the current state-of-the-art in the domain of representation learning for dynamic graphs. Second, we conduct a fair performance comparison among the most popular proposed approaches on node-and edge-level tasks, leveraging rigorous model selection and assessment for all the methods, thus establishing a sound baseline for evaluating new architectures and approaches.
With the explosive growth of online information, recommender systems play a key role to alleviate such information overload. Due to the important application value of recommender systems, there have always been emerging works in this field. In recommender systems, the main challenge is to learn the effective user/item representations from their interactions and side information (if any). Recently, graph neural network (GNN) techniques have been widely utilized in recommender systems since most of the information in recommender systems essentially has graph structure and GNN has superiority in graph representation learning. This article aims to provide a comprehensive review of recent research efforts on GNN-based recommender systems. Specifically, we provide a taxonomy of GNN-based recommendation models according to the types of information used and recommendation tasks. Moreover, we systematically analyze the challenges of applying GNN on different types of data and discuss how existing works in this field address these challenges. Furthermore, we state new perspectives pertaining to the development of this field. We collect the representative papers along with their open-source implementations in https://github.com/wusw14/GNN-in-RS.
Knowledge Graphs (KGs) have found many applications in industrial and in academic settings, which in turn, have motivated considerable research efforts towards large-scale information extraction from a variety of sources. Despite such efforts, it is well known that even the largest KGs suffer from incompleteness; Link Prediction (LP) techniques address this issue by identifying missing facts among entities already in the KG. Among the recent LP techniques, those based on KG embeddings have achieved very promising performance in some benchmarks. Despite the fast-growing literature on the subject, insufficient attention has been paid to the effect of the design choices in those methods. Moreover, the standard practice in this area is to report accuracy by aggregating over a large number of test facts in which some entities are vastly more represented than others; this allows LP methods to exhibit good results by just attending to structural properties that include such entities, while ignoring the remaining majority of the KG. This analysis provides a comprehensive comparison of embedding-based LP methods, extending the dimensions of analysis beyond what is commonly available in the literature. We experimentally compare the effectiveness and efficiency of 18 state-of-the-art methods, consider a rule-based baseline, and report detailed analysis over the most popular benchmarks in the literature.
Network embedding learns the vector representations of nodes. Most real world networks are heterogeneous and evolve over time. There are, however, no network embedding approaches designed for dynamic heterogeneous networks so far. Addressing this research gap is beneficial for analyzing and mining real world networks. We develop a novel representation learning method, change2vec, which considers a dynamic heterogeneous network as snapshots of networks with different time stamps. Instead of processing the whole network at each time stamp, change2vec models changes between two consecutive static networks by capturing newly-added and deleted nodes with their neighbour nodes as well as newly-formed or deleted edges that caused core structural changes known as triad closure or open processes. Change2vec leverages metapath based node embedding and change modeling to preserve both heterogeneous and dynamic features of a network. Experimental results show that change2vec outperforms two state-of-the-art methods in terms of clustering performance and efficiency.
Knowledge Graphs (KGs) are a popular means to represent knowledge on the Web, typically in the form of node/edge labelled directed graphs. We consider temporal KGs, in which edges are further annotated with time intervals, reflecting when the relationship between entities held in time. In this paper, we focus on the task of predicting time validity for unannotated edges. We introduce the problem as a variation of relational embedding. We adapt existing approaches, and explore the importance example selection and the incorporation of side information in the learning process. We present our experimental evaluation in details.
Currently there is no standard way to identify how a dataset was created, and what characteristics, motivations, and potential skews it represents. To begin to address this issue, we propose the concept of a datasheet for datasets, a short document to accompany public datasets, commercial APIs, and pretrained models. The goal of this proposal is to enable better communication between dataset creators and users, and help the AI community move toward greater transparency and accountability. By analogy, in computer hardware, it has become industry standard to accompany everything from the simplest components (e.g., resistors), to the most complex microprocessor chips, with datasheets detailing standard operating characteristics, test results, recommended usage, and other information. We outline some of the questions a datasheet for datasets should answer. These questions focus on when, where, and how the training data was gathered, its recommended use cases, and, in the case of human-centric datasets, information regarding the subjects' demographics and consent as applicable. We develop prototypes of datasheets for two well-known datasets: Labeled Faces in The Wild~\cite{lfw} and the Pang \& Lee Polarity Dataset~\cite{polarity}.
Networks are a fundamental tool for modeling complex systems in a variety of domains including social and communication networks as well as biology and neuroscience. The counts of small subgraph patterns in networks, called network motifs, are crucial to understanding the structure and function of these systems. However, the role of network motifs for temporal networks, which contain many timestamped links between nodes, is not well understood.
Here we develop a notion of a temporal network motif as an elementary unit of temporal networks and provide a general methodology for counting such motifs. We define temporal network motifs as induced subgraphs on sequences of edges, design several fast algorithms for counting temporal network motifs, and prove their runtime complexity. We also show that our fast algorithms achieve 1.3x to 56.5x speedups compared to a baseline method.
We use our algorithms to count temporal network motifs in a variety of real-world datasets. Results show that networks from different domains have significantly different motif frequencies, whereas networks from the same domain tend to have similar motif frequencies. We also find that measuring motif counts at various time scales reveals different behavior.
Relational machine learning studies methods for the statistical analysis of relational, or graph-structured, data. In this paper, we provide a review of how such statistical models can be “trained” on large knowledge graphs, and then used to predict new facts about the world (which is equivalent to predicting new edges in the graph). In particular, we discuss two fundamentally different kinds of statistical relational models, both of which can scale to massive data sets. The first is based on latent feature models such as tensor factorization and multiway neural networks. The second is based on mining observable patterns in the graph. We also show how to combine these latent and observable models to get improved modeling power at decreased computational cost. Finally, we discuss how such statistical models of graphs can be combined with text-based information extraction methods for automatically constructing knowledge graphs from the Web. To this end, we also discuss Google's knowledge vault project as an example of such combination.
The MovieLens datasets are widely used in education, research, and industry. They are downloaded hundreds of thousands of times each year, reflecting their use in popular press programming books, traditional and online courses, and software. These datasets are a product of member activity in the MovieLens movie recommendation system, an active research platform that has hosted many experiments since its launch in 1997. This article documents the history of MovieLens and the MovieLens datasets. We include a discussion of lessons learned from running a long-standing, live research platform from the perspective of a research organization. We document best practices and limitations of using the MovieLens datasets in new research.
Wikidata allows every user to extend and edit the stored information, even without creating an account. A form based interface makes editing easy. Wikidata's goal is to allow data to be used both in Wikipedia and in external applications. Data is exported through Web services in several formats, including JavaScript Object Notation, or JSON, and Resource Description Framework, or RDF. Data is published under legal terms that allow the widest possible reuse. The value of Wikipedia's data has long been obvious, with many efforts to use it. The Wikidata approach is to crowdsource data acquisition, allowing a global community to edit the data. This extends the traditional wiki approach of allowing users to edit a website. In March 2013, Wikimedia introduced Lua as a scripting language for automatically creating and enriching parts of articles. Lua scripts can access Wikidata, allowing Wikipedia editors to retrieve, process, and display data. Many other features were introduced in 2013, and development is planned to continue for the foreseeable future.
This paper presents a new approach for learning in structured domains (SDs) using a constructive neural network for graphs (NN4G). The new model allows the extension of the input domain for supervised neural networks to a general class of graphs including both acyclic/cyclic, directed/undirected labeled graphs. In particular, the model can realize adaptive contextual transductions, learning the mapping from graphs for both classification and regression tasks. In contrast to previous neural networks for structures that had a recursive dynamics, NN4G is based on a constructive feedforward architecture with state variables that uses neurons with no feedback connections. The neurons are applied to the input graphs by a general traversal process that relaxes the constraints of previous approaches derived by the causality assumption over hierarchical input data. Moreover, the incremental approach eliminates the need to introduce cyclic dependencies in the definition of the system state variables. In the traversal process, the NN4G units exploit (local) contextual information of the graphs vertices. In spite of the simplicity of the approach, we show that, through the compositionality of the contextual information developed by the learning, the model can deal with contextual information that is incrementally extended according to the graphs topology. The effectiveness and the generality of the new approach are investigated by analyzing its theoretical properties and providing experimental results.
Many underlying relationships among data in several areas of science and engineering, e.g., computer vision, molecular chemistry, molecular biology, pattern recognition, and data mining, can be represented in terms of graphs. In this paper, we propose a new neural network model, called graph neural network (GNN) model, that extends existing neural network methods for processing the data represented in graph domains. This GNN model, which can directly process most of the practically useful types of graphs, e.g., acyclic, cyclic, directed, and undirected, implements a function tau(G,n) isin IR
m
that maps a graph G and one of its nodes
n
into an
m
-dimensional Euclidean space. A supervised learning algorithm is derived to estimate the parameters of the proposed GNN model. The computational cost of the proposed algorithm is also considered. Some experimental results are shown to validate the proposed learning algorithm, and to demonstrate its generalization capabilities.
Software engineering event modeling using relative time in temporal knowledge graphs
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Long range graph benchmark
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Plover and polecat: A new political event ontology and dataset
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Relational learning on temporal knowledge graphs
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Explainable subgraph reasoning for forecasting on temporal knowledge graphs
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Graph hawkes neural network for forecasting on temporal knowledge graphs
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Ogb-lsc: A large-scale challenge for machine learning on graphs
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Open graph benchmark: Datasets for machine learning on graphs
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Representation learning for dynamic graphs: A survey
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A novel explainable link forecasting framework for temporal knowledge graphs using time-relaxed cyclic and acyclic rules
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Gdelt: Global data on events, location, and tone
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