Graph Neural Networks and its applications

This project will explore some of the most prominent Graph Neural Network variants and apply them to two tasks: approximation of the community detection Girvan-Newman algorithm and compiled code snippet classification

Learning Effective Embeddings for Dynamic Graphs and Quantifying Graph Embedding Interpretability

Graph representation learning has been a very active research area in recent years. The goal of graph representation learning is to generate representation vectors that accurately capture the structure and features of large graphs. This is especially important because the quality of the graph representation vectors will affect the performance of these vectors in downstream tasks such as node classification and link prediction. Many techniques have been proposed for generating effective graph representation vectors. These methods can be applied to both static and dynamic graphs. A static graph is a single fixed graph, while a dynamic graph evolves over time, and its nodes and edges can be added or deleted from the graph. We surveyed the graph embedding methods for both static and dynamic graphs. The majority of the existing graph embedding methods are developed for static graphs. Therefore, since most real-world graphs are dynamic, developing novel graph embedding methods suitable for evolving graphs is essential. This dissertation proposes three dynamic graph embedding models. In previous dynamic methods, the inputs were mainly adjacency matrices of graphs which are not memory efficient and may not capture the neighbourhood structure in graphs effectively. Therefore, we developed Dynnode2vec based on random walks using the static model Node2vec. Dynnode2vec generates node embeddings in each snapshot by initializing the current model with previous embedding vectors and training the model using a set of random walks obtained for nodes in the snapshot. Our second model, LSTM-Node2vec, is also based on random walks. This method leverages the LSTM model to capture the long-range dependencies between nodes in combination with Node2vec to generate node embeddings. Finally, inspired by the importance of substructures in the graphs, our third model TGR-Clique generates node embeddings by considering the effects of neighbours of a node in the maximal cliques containing the node. Experiments on real-world datasets demonstrate the effectiveness of our proposed methods in comparison to the state-of-the-art models. In addition, motivated by the lack of proper measures for quantifying and comparing graph embeddings interpretability, we proposed two interpretability measures for graph embeddings using the centrality properties of graphs

Unsupervised Structural Embedding Methods for Efficient Collective Network Mining

How can we align accounts of the same user across social networks? Can we identify the professional role of an email user from their patterns of communication? Can we predict the medical effects of chemical compounds from their atomic network structure? Many problems in graph data mining, including all of the above, are defined on multiple networks. The central element to all of these problems is cross-network comparison, whether at the level of individual nodes or entities in the network or at the level of entire networks themselves. To perform this comparison meaningfully, we must describe the entities in each network expressively in terms of patterns that generalize across the networks. Moreover, because the networks in question are often very large, our techniques must be computationally efficient. In this thesis, we propose scalable unsupervised methods that embed nodes in vector space by mapping nodes with similar structural roles in their respective networks, even if they come from different networks, to similar parts of the embedding space. We perform network alignment by matching nodes across two or more networks based on the similarity of their embeddings, and refine this process by reinforcing the consistency of each node’s alignment with those of its neighbors. By characterizing the distribution of node embeddings in a graph, we develop graph-level feature vectors that are highly effective for graph classification. With principled sparsification and randomized approximation techniques, we make all our methods computationally efficient and able to scale to graphs with millions of nodes or edges. We demonstrate the effectiveness of structural node embeddings on industry-scale applications, and propose an extensive set of embedding evaluation techniques that lay the groundwork for further methodological development and application.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/162895/1/mheimann_1.pd

Genetic Meta-Structure Search for Recommendation on Heterogeneous Information Network

In the past decade, the heterogeneous information network (HIN) has become an important methodology for modern recommender systems. To fully leverage its power, manually designed network templates, i.e., meta-structures, are introduced to filter out semantic-aware information. The hand-crafted meta-structure rely on intense expert knowledge, which is both laborious and data-dependent. On the other hand, the number of meta-structures grows exponentially with its size and the number of node types, which prohibits brute-force search. To address these challenges, we propose Genetic Meta-Structure Search (GEMS) to automatically optimize meta-structure designs for recommendation on HINs. Specifically, GEMS adopts a parallel genetic algorithm to search meaningful meta-structures for recommendation, and designs dedicated rules and a meta-structure predictor to efficiently explore the search space. Finally, we propose an attention based multi-view graph convolutional network module to dynamically fuse information from different meta-structures. Extensive experiments on three real-world datasets suggest the effectiveness of GEMS, which consistently outperforms all baseline methods in HIN recommendation. Compared with simplified GEMS which utilizes hand-crafted meta-paths, GEMS achieves over $6\%$ performance gain on most evaluation metrics. More importantly, we conduct an in-depth analysis on the identified meta-structures, which sheds light on the HIN based recommender system design.Comment: Published in Proceedings of the 29th ACM International Conference on Information and Knowledge Management (CIKM '20

동종, 이종, 그리고 나무 형태의 그래프를 위한 비지도 표현 학습

학위논문(박사) -- 서울대학교대학원 : 공과대학 전기·정보공학부, 2022. 8. 최진영.그래프 데이터에 대한 비지도 표현 학습의 목적은 그래프의 구조와 노드의 속성을 잘 반영하는 유용한 노드 단위 혹은 그래프 단위의 벡터 형태 표현을 학습하는 것이다. 최근, 그래프 데이터에 대해 강력한 표현 학습 능력을 갖춘 그래프 신경망을 활용한 비지도 그래프 표현 학습 모델의 설계가 주목을 받고 있다. 많은 방법들은 한 종류의 엣지와 한 종류의 노드가 존재하는 동종 그래프에 대한 학습에 집중을 한다. 하지만 이 세상에 수많은 종류의 관계가 존재하기 때문에, 그래프 또한 구조적, 의미론적 속성을 통해 다양한 종류로 분류할 수 있다. 그래서, 그래프로부터 유용한 표현을 학습하기 위해서는 비지도 학습 프레임워크는 입력 그래프의 특징을 제대로 고려해야만 한다. 본 학위논문에서 우리는 널리 접할 수 있는 세가지 그래프 구조인 동종 그래프, 트리 형태의 그래프, 그리고 이종 그래프에 대한 그래프 신경망을 활용하는 비지도 학습 모델들을 제안한다. 처음으로, 우리는 동종 그래프의 노드에 대하여 저차원 표현을 학습하는 그래프 컨볼루션 오토인코더 모델을 제안한다. 기존의 그래프 오토인코더는 구조의 전체가 학습이 불가능해서 제한적인 표현 학습 능력을 가질 수 있는 반면에, 제안하는 오토인코더는 노드의 피쳐를 복원하며,구조의 전체가 학습이 가능하다. 노드의 피쳐를 복원하기 위해서, 우리는 인코더 부분의 역할이 이웃한 노드끼리 유사한 표현을 가지게 하는 라플라시안 스무딩이라는 것에 주목하여 디코더 부분에서는 이웃 노드의 표현과 멀어지게 하는 라플라시안 샤프닝을 하도록 설계하였다. 또한 라플라시안 샤프닝을 그대로 적용하면 불안정성을 유발할 수 있기 때문에, 엣지의 가중치 값에 음의 값을 줄 수 있는 부호형 그래프를 활용하여 안정적인 라플라시안 샤프닝의 형태를 제안하였다. 동종 그래프에 대한 노드 클러스터링과 링크 예측 실험을 통하여 제안하는 방법이 안정적으로 우수한 성능을 보임을 확인하였다. 둘째로, 우리는 트리의 형태를 가지는 계층적인 관계를 가지고 있는 그래프의 노드 표현을 정확하게 학습하기 위하여 쌍곡선 공간에서 동작하는 오토인코더 모델을 제안한다. 유클리디언 공간은 트리를 사상하기에 부적절하다는 최근의 분석을 통하여, 쌍곡선 공간에서 그래프 신경망의 레이어를 활용하여 노드의 저차원 표현을 학습하게 된다. 이 때, 그래프 신경망이 쌍곡선 기하학에서 계층 정보를 담고 있는 거리의 값을 활용하여 노드의 이웃사이의 중요도를 활용하도록 설계하였다. 우리는 논문 인용 관계 네트워크, 계통도, 이미지 사이의 네트워크등에 대해 제안한 모델을 적용하여 노드 클러스터링과 링크 예측 실험을 하였으며, 트리의 형태를 가지는 그래프에 대해서 제안한 모델이 유클리디언 공간에서 수행하는 모델에 비해 향상된 성능을 보였다는 것을 확인하였다. 마지막으로, 우리는 여러 종류의 노드와 엣지를 가지는 이종그래프에 대한 대조 학습 모델을 제안한다. 우리는 기존의 방법들이 학습하기 이전에 충분한 도메인 지식을 사용하여 설계한 메타패스나 메타그래프에 의존한다는 단점과 많은 이종그래프의 엣지가 다른 노드 종류사이의 관계에 집중하고 있다는 점을 주목하였다. 이를 통해 우리는 사전과정이 필요없으며 다른 종류 사이의 관계에 더하여 같은 종류 사이의 관계도 동시에 효율적으로 학습하게 하는 메타노드라는 개념을 제안하였다. 또한 메타노드를 기반으로하는 그래프 신경망과 대조 학습 모델을 제안하였다. 우리는 제안한 모델을 메타패스를 사용하는 이종그래프 학습 모델과 노드 클러스터링 등의 실험 성능으로 비교해보았을 때, 비등하거나 높은 성능을 보였음을 확인하였다.The goal of unsupervised graph representation learning is extracting useful node-wise or graph-wise vector representation that is aware of the intrinsic structures of the graph and its attributes. These days, designing methodology of unsupervised graph representation learning based on graph neural networks has growing attention due to their powerful representation ability. Many methods are focused on a homogeneous graph that is a network with a single type of node and a single type of edge. However, as many types of relationships exist in this world, graphs can also be classified into various types by structural and semantic properties. For this reason, to learn useful representations from graphs, the unsupervised learning framework must consider the characteristics of the input graph. In this dissertation, we focus on designing unsupervised learning models using graph neural networks for three graph structures that are widely available: homogeneous graphs, tree-like graphs, and heterogeneous graphs. First, we propose a symmetric graph convolutional autoencoder which produces a low-dimensional latent representation from a homogeneous graph. In contrast to the existing graph autoencoders with asymmetric decoder parts, the proposed autoencoder has a newly designed decoder which builds a completely symmetric autoencoder form. For the reconstruction of node features, the decoder is designed based on Laplacian sharpening as the counterpart of Laplacian smoothing of the encoder, which allows utilizing the graph structure in the whole processes of the proposed autoencoder architecture. In order to prevent the numerical instability of the network caused by the Laplacian sharpening introduction, we further propose a new numerically stable form of the Laplacian sharpening by incorporating the signed graphs. The experimental results of clustering, link prediction and visualization tasks on homogeneous graphs strongly support that the proposed model is stable and outperforms various state-of-the-art algorithms. Second, we analyze how unsupervised tasks can benefit from learned representations in hyperbolic space. To explore how well the hierarchical structure of unlabeled data can be represented in hyperbolic spaces, we design a novel hyperbolic message passing autoencoder whose overall auto-encoding is performed in hyperbolic space. The proposed model conducts auto-encoding the networks via fully utilizing hyperbolic geometry in message passing. Through extensive quantitative and qualitative analyses, we validate the properties and benefits of the unsupervised hyperbolic representations of tree-like graphs. Third, we propose the novel concept of metanode for message passing to learn both heterogeneous and homogeneous relationships between any two nodes without meta-paths and meta-graphs. Unlike conventional methods, metanodes do not require a predetermined step to manipulate the given relations between different types to enrich relational information. Going one step further, we propose a metanode-based message passing layer and a contrastive learning model using the proposed layer. In our experiments, we show the competitive performance of the proposed metanode-based message passing method on node clustering and node classification tasks, when compared to state-of-the-art methods for message passing networks for heterogeneous graphs.1 Introduction 1 2 Representation Learning on Graph-Structured Data 4 2.1 Basic Introduction 4 2.1.1 Notations 5 2.2 Traditional Approaches 5 2.2.1 Graph Statistic 5 2.2.2 Neighborhood Overlap 7 2.2.3 Graph Kernel 9 2.2.4 Spectral Approaches 10 2.3 Node Embeddings I: Factorization and Random Walks 15 2.3.1 Factorization-based Methods 15 2.3.2 Random Walk-based Methods 16 2.4 Node Embeddings II: Graph Neural Networks 17 2.4.1 Overview of Framework 17 2.4.2 Representative Models 18 2.5 Learning in Unsupervised Environments 21 2.5.1 Predictive Coding 21 2.5.2 Contrastive Coding 22 2.6 Applications 24 2.6.1 Classifications 24 2.6.2 Link Prediction 26 3 Autoencoder Architecture for Homogeneous Graphs 27 3.1 Overview 27 3.2 Preliminaries 30 3.2.1 Spectral Convolution on Graphs 30 3.2.2 Laplacian Smoothing 32 3.3 Methodology 33 3.3.1 Laplacian Sharpening 33 3.3.2 Numerically Stable Laplacian Sharpening 34 3.3.3 Subspace Clustering Cost for Image Clustering 37 3.3.4 Training 39 3.4 Experiments 40 3.4.1 Datasets 40 3.4.2 Experimental Settings 42 3.4.3 Comparing Methods 42 3.4.4 Node Clustering 43 3.4.5 Image Clustering 45 3.4.6 Ablation Studies 46 3.4.7 Link Prediction 47 3.4.8 Visualization 47 3.5 Summary 49 4 Autoencoder Architecture for Tree-like Graphs 50 4.1 Overview 50 4.2 Preliminaries 52 4.2.1 Hyperbolic Embeddings 52 4.2.2 Hyperbolic Geometry 53 4.3 Methodology 55 4.3.1 Geometry-Aware Message Passing 56 4.3.2 Nonlinear Activation 57 4.3.3 Loss Function 58 4.4 Experiments 58 4.4.1 Datasets 59 4.4.2 Compared Methods 61 4.4.3 Experimental Details 62 4.4.4 Node Clustering and Link Prediction 64 4.4.5 Image Clustering 66 4.4.6 Structure-Aware Unsupervised Embeddings 68 4.4.7 Hyperbolic Distance to Filter Training Samples 71 4.4.8 Ablation Studies 74 4.5 Further Discussions 75 4.5.1 Connection to Contrastive Learning 75 4.5.2 Failure Cases of Hyperbolic Embedding Spaces 75 4.6 Summary 77 5 Contrastive Learning for Heterogeneous Graphs 78 5.1 Overview 78 5.2 Preliminaries 82 5.2.1 Meta-path 82 5.2.2 Representation Learning on Heterogeneous Graphs 82 5.2.3 Contrastive methods for Heterogeneous Graphs 83 5.3 Methodology 84 5.3.1 Definitions 84 5.3.2 Metanode-based Message Passing Layer 86 5.3.3 Contrastive Learning Framework 88 5.4 Experiments 89 5.4.1 Experimental Details 90 5.4.2 Node Classification 94 5.4.3 Node Clustering 96 5.4.4 Visualization 96 5.4.5 Effectiveness of Metanodes 97 5.5 Summary 99 6 Conclusions 101박

Machine learning for managing structured and semi-structured data

As the digitalization of private, commercial, and public sectors advances rapidly, an increasing amount of data is becoming available. In order to gain insights or knowledge from these enormous amounts of raw data, a deep analysis is essential. The immense volume requires highly automated processes with minimal manual interaction. In recent years, machine learning methods have taken on a central role in this task. In addition to the individual data points, their interrelationships often play a decisive role, e.g. whether two patients are related to each other or whether they are treated by the same physician. Hence, relational learning is an important branch of research, which studies how to harness this explicitly available structural information between different data points. Recently, graph neural networks have gained importance. These can be considered an extension of convolutional neural networks from regular grids to general (irregular) graphs. Knowledge graphs play an essential role in representing facts about entities in a machine-readable way. While great efforts are made to store as many facts as possible in these graphs, they often remain incomplete, i.e., true facts are missing. Manual verification and expansion of the graphs is becoming increasingly difficult due to the large volume of data and must therefore be assisted or substituted by automated procedures which predict missing facts. The field of knowledge graph completion can be roughly divided into two categories: Link Prediction and Entity Alignment. In Link Prediction, machine learning models are trained to predict unknown facts between entities based on the known facts. Entity Alignment aims at identifying shared entities between graphs in order to link several such knowledge graphs based on some provided seed alignment pairs. In this thesis, we present important advances in the field of knowledge graph completion. For Entity Alignment, we show how to reduce the number of required seed alignments while maintaining performance by novel active learning techniques. We also discuss the power of textual features and show that graph-neural-network-based methods have difficulties with noisy alignment data. For Link Prediction, we demonstrate how to improve the prediction for unknown entities at training time by exploiting additional metadata on individual statements, often available in modern graphs. Supported with results from a large-scale experimental study, we present an analysis of the effect of individual components of machine learning models, e.g., the interaction function or loss criterion, on the task of link prediction. We also introduce a software library that simplifies the implementation and study of such components and makes them accessible to a wide research community, ranging from relational learning researchers to applied fields, such as life sciences. Finally, we propose a novel metric for evaluating ranking results, as used for both completion tasks. It allows for easier interpretation and comparison, especially in cases with different numbers of ranking candidates, as encountered in the de-facto standard evaluation protocols for both tasks.Mit der rasant fortschreitenden Digitalisierung des privaten, kommerziellen und öffentlichen Sektors werden immer größere Datenmengen verfügbar. Um aus diesen enormen Mengen an Rohdaten Erkenntnisse oder Wissen zu gewinnen, ist eine tiefgehende Analyse unerlässlich. Das immense Volumen erfordert hochautomatisierte Prozesse mit minimaler manueller Interaktion. In den letzten Jahren haben Methoden des maschinellen Lernens eine zentrale Rolle bei dieser Aufgabe eingenommen. Neben den einzelnen Datenpunkten spielen oft auch deren Zusammenhänge eine entscheidende Rolle, z.B. ob zwei Patienten miteinander verwandt sind oder ob sie vom selben Arzt behandelt werden. Daher ist das relationale Lernen ein wichtiger Forschungszweig, der untersucht, wie diese explizit verfügbaren strukturellen Informationen zwischen verschiedenen Datenpunkten nutzbar gemacht werden können. In letzter Zeit haben Graph Neural Networks an Bedeutung gewonnen. Diese können als eine Erweiterung von CNNs von regelmäßigen Gittern auf allgemeine (unregelmäßige) Graphen betrachtet werden. Wissensgraphen spielen eine wesentliche Rolle bei der Darstellung von Fakten über Entitäten in maschinenlesbaren Form. Obwohl große Anstrengungen unternommen werden, so viele Fakten wie möglich in diesen Graphen zu speichern, bleiben sie oft unvollständig, d. h. es fehlen Fakten. Die manuelle Überprüfung und Erweiterung der Graphen wird aufgrund der großen Datenmengen immer schwieriger und muss daher durch automatisierte Verfahren unterstützt oder ersetzt werden, die fehlende Fakten vorhersagen. Das Gebiet der Wissensgraphenvervollständigung lässt sich grob in zwei Kategorien einteilen: Link Prediction und Entity Alignment. Bei der Link Prediction werden maschinelle Lernmodelle trainiert, um unbekannte Fakten zwischen Entitäten auf der Grundlage der bekannten Fakten vorherzusagen. Entity Alignment zielt darauf ab, gemeinsame Entitäten zwischen Graphen zu identifizieren, um mehrere solcher Wissensgraphen auf der Grundlage einiger vorgegebener Paare zu verknüpfen. In dieser Arbeit stellen wir wichtige Fortschritte auf dem Gebiet der Vervollständigung von Wissensgraphen vor. Für das Entity Alignment zeigen wir, wie die Anzahl der benötigten Paare reduziert werden kann, während die Leistung durch neuartige aktive Lerntechniken erhalten bleibt. Wir erörtern auch die Leistungsfähigkeit von Textmerkmalen und zeigen, dass auf Graph-Neural-Networks basierende Methoden Schwierigkeiten mit verrauschten Paar-Daten haben. Für die Link Prediction demonstrieren wir, wie die Vorhersage für unbekannte Entitäten zur Trainingszeit verbessert werden kann, indem zusätzliche Metadaten zu einzelnen Aussagen genutzt werden, die oft in modernen Graphen verfügbar sind. Gestützt auf Ergebnisse einer groß angelegten experimentellen Studie präsentieren wir eine Analyse der Auswirkungen einzelner Komponenten von Modellen des maschinellen Lernens, z. B. der Interaktionsfunktion oder des Verlustkriteriums, auf die Aufgabe der Link Prediction. Außerdem stellen wir eine Softwarebibliothek vor, die die Implementierung und Untersuchung solcher Komponenten vereinfacht und sie einer breiten Forschungsgemeinschaft zugänglich macht, die von Forschern im Bereich des relationalen Lernens bis hin zu angewandten Bereichen wie den Biowissenschaften reicht. Schließlich schlagen wir eine neuartige Metrik für die Bewertung von Ranking-Ergebnissen vor, wie sie für beide Aufgaben verwendet wird. Sie ermöglicht eine einfachere Interpretation und einen leichteren Vergleich, insbesondere in Fällen mit einer unterschiedlichen Anzahl von Kandidaten, wie sie in den de-facto Standardbewertungsprotokollen für beide Aufgaben vorkommen

Computational Labeling, Partitioning, and Balancing of Molecular Networks

Recent advances in high throughput techniques enable large-scale molecular quantification with high accuracy, including mRNAs, proteins and metabolites. Differential expression of these molecules in case and control samples provides a way to select phenotype-associated molecules with statistically significant changes. However, given the significance ranking list of molecular changes, how those molecules work together to drive phenotype formation is still unclear. In particular, the changes in molecular quantities are insufficient to interpret the changes in their functional behavior. My study is aimed at answering this question by integrating molecular network data to systematically model and estimate the changes of molecular functional behaviors. We build three computational models to label, partition, and balance molecular networks using modern machine learning techniques. (1) Due to the incompleteness of protein functional annotation, we develop AptRank, an adaptive PageRank model for protein function prediction on bilayer networks. By integrating Gene Ontology (GO) hierarchy with protein-protein interaction network, our AptRank outperforms four state-of-the-art methods in a comprehensive evaluation using benchmark datasets. (2) We next extend our AptRank into a network partitioning method, BioSweeper, to identify functional network modules in which molecules share similar functions and also densely connect to each other. Compared to traditional network partitioning methods using only network connections, BioSweeper, which integrates the GO hierarchy, can automatically identify functionally enriched network modules. (3) Finally, we conduct a differential interaction analysis, namely difFBA, on protein-protein interaction networks by simulating protein fluxes using flux balance analysis (FBA). We test difFBA using quantitative proteomic data from colon cancer, and demonstrate that difFBA offers more insights into functional changes in molecular behavior than does protein quantity changes alone. We conclude that our integrative network model increases the observational dimensions of complex biological systems, and enables us to more deeply understand the causal relationships between genotypes and phenotypes

A Survey on Malware Detection with Graph Representation Learning

Malware detection has become a major concern due to the increasing number and complexity of malware. Traditional detection methods based on signatures and heuristics are used for malware detection, but unfortunately, they suffer from poor generalization to unknown attacks and can be easily circumvented using obfuscation techniques. In recent years, Machine Learning (ML) and notably Deep Learning (DL) achieved impressive results in malware detection by learning useful representations from data and have become a solution preferred over traditional methods. More recently, the application of such techniques on graph-structured data has achieved state-of-the-art performance in various domains and demonstrates promising results in learning more robust representations from malware. Yet, no literature review focusing on graph-based deep learning for malware detection exists. In this survey, we provide an in-depth literature review to summarize and unify existing works under the common approaches and architectures. We notably demonstrate that Graph Neural Networks (GNNs) reach competitive results in learning robust embeddings from malware represented as expressive graph structures, leading to an efficient detection by downstream classifiers. This paper also reviews adversarial attacks that are utilized to fool graph-based detection methods. Challenges and future research directions are discussed at the end of the paper.Comment: Preprint, submitted to ACM Computing Surveys on March 2023. For any suggestions or improvements, please contact me directly by e-mai