With the rapid emergence of graph representation learning, the construction of new large-scale datasets is necessary to distinguish model capabilities and accurately assess the strengths and weaknesses of each technique. By carefully analyzing existing graph databases, we identify 3 critical components important for advancing the field of graph representation learning: (1) large graphs, (2) many graphs, and (3) class diversity. To date, no single graph database offers all these desired properties. We introduce MalNet, the largest public graph database ever constructed, representing a large-scale ontology of malicious software function call graphs. MalNet contains over 1.2 million graphs, averaging over 15k nodes and 35k edges per graph, across a hierarchy of 47 types and 696 families. Compared to the popular REDDIT-12K database, MalNet offers 105x more graphs, 39x larger graphs on average, and 63x more classes. We provide a detailed analysis of MalNet, discussing its properties and provenance, along with the evaluation of state-of-the-art machine learning and graph neural network techniques. The unprecedented scale and diversity of MalNet offers exciting opportunities to advance the frontiers of graph representation learning--enabling new discoveries and research into imbalanced classification, explainability and the impact of class hardness. The database is publicly available at www.mal-net.org.
The majority of data scientists and machine learning practitioners use relational data in their work [State of ML and Data Science 2017, Kaggle, Inc.]. But training machine learning models on data stored in relational databases requires significant data extraction and feature engineering efforts. These efforts are not only costly, but they also destroy potentially important relational structure in the data. We introduce a method that uses Graph Neural Networks to overcome these challenges. Our proposed method outperforms state-of-the-art automatic feature engineering methods on two out of thre…
Hunan Univerisity（湖南大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Tsinghua University（清华大学）；University of Connecticut（康涅狄格大学）
Top Ten Challenges Towards Agentic Neural Graph Databases
Department of Computer Science and Engineering, HKUST（香港科技大学计算机科学与工程学系）；Department of Mathematical Sciences, Tsinghua University（清华大学数学科学系）；AI Group, WeBank（微众银行AI团队）；Institute of Artificial Intelligence, Beihang University（北京航空航天大学人工智能研究院）；Wuhan University（武汉大学）；Sichuan University（四川大学）
Bayesian approaches to learn the graphical structure of Bayesian Belief Networks (BBNs) from databases share the assumption that the database is complete, that is, no entry is reported as unknown. Attempts to relax this assumption involve the use of expensive iterative methods to discriminate among different structures. This paper introduces a deterministic method to learn the graphical structure of a BBN from a possibly incomplete database. Experimental evaluations show a significant robustness of this method and a remarkable independence of its execution time from the number of missing data.
Towards Foundation Models for Relational Databases with Language Models and Graph Neural Networks
University of Stuttgart, Stuttgart, Germany（斯图加特大学）；Internet Science Research Group, University of Southampton, Southampton, United Kingdom（互联网科学研究组，南安普顿大学）
Within a large database G containing graphs with labeled nodes and directed, multi-edges; how can we detect the anomalous graphs? Most existing work are designed for plain (unlabeled) and/or simple (unweighted) graphs. We introduce CODETECT, the first approach that addresses the anomaly detection task for graph databases with such complex nature. To this end, it identifies a small representative set S of structural patterns (i.e., node-labeled network motifs) that losslessly compress database G as concisely as possible. Graphs that do not compress well are flagged as anomalous. CODETECT exhibi…
In a business-to-business (B2B) customer relationship management (CRM) use case, each client is a potential business organization/company with a solid business strategy and focused and rational decisions. This paper introduces a graph-based analytics approach to improve CRM within a B2B environment. In our approach, in the first instance, we have designed a graph database using the Neo4j platform. Secondly, the graph database has been investigated by using data mining and exploratory analysis coupled with cypher graph query language. Specifically, we have applied the graph convolution network …
We present Column2Vec, a distributed representation of database columns based on column metadata. Our distributed representation has several applications. Using known names for groups of columns (i.e., a table name), we train a model to generate an appropriate name for columns in an unnamed table. We demonstrate the viability of our approach using schema information collected from open source applications on GitHub.
Learning Federated Neural Graph Databases for Answering Complex Queries from Distributed Knowledge Graphs
The Hong Kong University of Science and Technology（香港科技大学）；Nanyang Technological University（南洋理工大学）；Zhongguancun Laboratory（中关村实验室）；WeBank；Beihang University（北航大学）
In this paper, we address the problem of learning low dimension representation of entities on relational databases consisting of multiple tables. Embeddings help to capture semantics encoded in the database and can be used in a variety of settings like auto-completion of tables, fully-neural query processing of relational joins queries, seamlessly handling missing values, and more. Current work is restricted to working with just single table, or using pretrained embeddings over an external corpus making them unsuitable for use in real-world databases. In this work, we look into ways of using t…
Stanford University（斯坦福大学）；Intel AI Lab（英特尔人工智能实验室）；Vrije Universiteit Amsterdam（阿姆斯特丹自由大学）；Mila - Québec AI Institute（米拉-魁北克人工智能研究所）；Université de Montréal（蒙特利尔大学）
Complex logical query answering (CLQA) is a recently emerged task of graph machine learning that goes beyond simple one-hop link prediction and solves a far more complex task of multi-hop logical reasoning over massive, potentially incomplete graphs in a latent space. The task received a significant traction in the community; numerous works expanded the field along theoretical and practical axes to tackle different types of complex queries and graph modalities with efficient systems. In this paper, we provide a holistic survey of CLQA with a detailed taxonomy studying the field from multiple a…
Saitama University（埼玉大学）；University of Tokyo（东京大学）；Tohoku University（东北大学）；Okayama University（冈山大学）
The open-source library, irbasis, provides easy-to-use tools for two sets of orthogonal functions named intermediate representation (IR). The IR basis enables a compact representation of the Matsubara Green's function and efficient calculations of quantum models. The IR basis functions are defined as the solution of an integral equation whose analytical solution is not available for this moment. The library consists of a database of pre-computed high-precision numerical solutions and computational code for evaluating the functions from the database. This paper describes technical details and d…
RKMV Educational and Research Institute（RKMV教育与研究院）
Many data science applications like social network analysis use graphs as their primary form of data. However, acquiring graph-structured data from social media presents some interesting challenges. The first challenge is the high data velocity and bursty nature of the social media data. The second challenge is that the complex nature of the data makes the ingestion process expensive. If we want to store the streaming graph data in a graph database, we face a third challenge -- the database is very often unable to sustain the ingestion of high-velocity, high-burst data. We have developed an ad…
We address the problem of learning a distributed representation of entities in a relational database using a low-dimensional embedding. Low-dimensional embeddings aim to encapsulate a concise vector representation for an underlying dataset with minimum loss of information. Embeddings across entities in a relational database have been less explored due to the intricate data relations and representation complexity involved. Relational databases are an inter-weaved collection of relations that not only model relationships between entities but also record complex domain-specific quantitative and t…
INRIA（法国国家信息与自动化研究所）；ENS（巴黎高等师范学院）；PSL Research University（PSL研究大学）
We study the fundamental limits for reconstruction in weighted graph (or matrix) database alignment. We consider a model of two graphs where $π^*$ is a planted uniform permutation and all pairs of edge weights $(A_{i,j}, B_{π^*(i),π^*(j)})_{1 \leq i<j \leq n}$ are i.i.d. pairs of Gaussian variables with zero mean, unit variance and correlation parameter $ρ\in [0,1]$. We prove that there is a sharp threshold for exact recovery of $π^*$: if $n ρ^2 \geq (4+ε) \log n + ω(1)$ for some $ε>0$, there is an estimator $\hatπ$ -- namely the MAP estimator -- based on the observation of databases $A,B$ tha…
A New Workflow for Materials Discovery Bridging the Gap Between Experimental Databases and Graph Neural Networks
Department of Physics, University at Buffalo, State University of New York, Buffalo, NY；Department of Computer Science \& Engineering, University at Buffalo, State University of New York, Buffalo, NY；Institute for Artificial Intelligence；Data Science, University at Buffalo, State University of New York, Buffalo, NY
Georgia State University（佐治亚州立大学）；Hunan Normal University（湖南师范大学）；Anhui University（安徽大学）
We propose an approach of graph convolutional networks for robust infant cry classification. We construct non-fully connected graphs based on the similarities among the relevant nodes in both supervised and semi-supervised node classification with convolutional neural networks to consider the short-term and long-term effects of infant cry signals related to inner-class and inter-class messages. The approach captures the diversity of variations within infant cries, especially for limited training samples. The effectiveness of this approach is evaluated on Baby Chillanto Database and Baby2020 da…
