Search Efficient Binary Network Embedding

Traditional network embedding primarily focuses on learning a dense vector representation for each node, which encodes network structure and/or node content information, such that off-the-shelf machine learning algorithms can be easily applied to the vector-format node representations for network analysis. However, the learned dense vector representations are inefficient for large-scale similarity search, which requires to find the nearest neighbor measured by Euclidean distance in a continuous vector space. In this paper, we propose a search efficient binary network embedding algorithm called BinaryNE to learn a sparse binary code for each node, by simultaneously modeling node context relations and node attribute relations through a three-layer neural network. BinaryNE learns binary node representations efficiently through a stochastic gradient descent based online learning algorithm. The learned binary encoding not only reduces memory usage to represent each node, but also allows fast bit-wise comparisons to support much quicker network node search compared to Euclidean distance or other distance measures. Our experiments and comparisons show that BinaryNE not only delivers more than 23 times faster search speed, but also provides comparable or better search quality than traditional continuous vector based network embedding methods

Augmented network embedding in attributed graphs

University of Technology Sydney. Faculty of Engineering and Information Technology.With the widespread use of information technologies, information networks are becoming increasingly popular to capture complex relationships across various disciplines, such as social networks, citation networks, telecommunication networks, and biological networks. Analyzing these networks sheds light on different aspects of social life, such as the structure of societies, information diffusion, and communication patterns. In reality, however, the large scale of information networks often makes network analytic tasks computationally expensive or intractable. Network embedding has been recently proposed as a new learning paradigm to embed network nodes into a low-dimensional vector space. This facilitates the original network to be easily handled in the new vector space for further analysis. Existing research on network embedding mainly focuses on capturing the structure relatedness in the embedding space, while ignores the important information carried by the widely existing node attributes and labels, which limited the network embedding performance significantly. In this thesis, we dealt with the research problem of augmented network embedding in attributed graphs that aims to learn informative node vector-format representations by augmenting network topology structure with node content attributes and node labels if available. We summarized four research challenges in augmented network embedding: (1) caused by the discrepancy between network structure and node attributes/labels; (2) in network structure and node attributes/labels; (3) for handling large-scale networks; (4) for directly benefiting specific network analytic tasks. To overcome the above challenges, we proposed a series of augmented network embedding algorithms in this thesis. To handle the challenge, we proposed the HSCA algorithm that effectively encodes the similarity measured by homophily, structural context and node content attributes into a unified node representation through the regularized inductive matrix factorization. The attri2vec algorithm was then proposed to address the and challenges, in which node representations are learned by discovering an attribute subspace that better respects network structure. For handling large-scale incomplete networks, we proposed the SINE algorithm that learns node representations by simultaneously modeling node-neighbor and node-attribute relations through a three-layer neural network, with an efficient Stochastic Gradient Descent based online learning strategy. The above three augmented network embedding algorithms only augment network structure with node content attributes, with the purpose to obtain more informative network representations. They are unsupervised, task-general and incapable of directly benefiting specific tasks. To seamlessly integrate network embedding with network analytic tasks, we proposed two task-orientated network embedding algorithms. For collective classification on sparsely labeled networks, we proposed the discriminative attributed network embedding algorithm DMF that integrates network embedding with an empirical loss minimization for classifying node labels, with the purpose of simultaneously exerting the discriminative power of node labels and informativeness of node representations. For searching similar nodes efficiently on large-scale networks, BinaryNE was proposed to learn binary node representations from network structure and node content attributes so that node similarity search can be efficiently done through the fast bitwise Hamming distance calculation performed on the learned binary node representations. To verify the effectiveness of the proposed algorithms, extensive experiments were carried out on nine real-world attributed networks, showing the advantage of the proposed algorithms over state-of-the-art baselines

Towards Complex Dynamic Physics System Simulation with Graph Neural ODEs

The great learning ability of deep learning models facilitates us to comprehend the real physical world, making learning to simulate complicated particle systems a promising endeavour. However, the complex laws of the physical world pose significant challenges to the learning based simulations, such as the varying spatial dependencies between interacting particles and varying temporal dependencies between particle system states in different time stamps, which dominate particles' interacting behaviour and the physical systems' evolution patterns. Existing learning based simulation methods fail to fully account for the complexities, making them unable to yield satisfactory simulations. To better comprehend the complex physical laws, this paper proposes a novel learning based simulation model- Graph Networks with Spatial-Temporal neural Ordinary Equations (GNSTODE)- that characterizes the varying spatial and temporal dependencies in particle systems using a united end-to-end framework. Through training with real-world particle-particle interaction observations, GNSTODE is able to simulate any possible particle systems with high precisions. We empirically evaluate GNSTODE's simulation performance on two real-world particle systems, Gravity and Coulomb, with varying levels of spatial and temporal dependencies. The results show that the proposed GNSTODE yields significantly better simulations than state-of-the-art learning based simulation methods, which proves that GNSTODE can serve as an effective solution to particle simulations in real-world application.Comment: 12 pages,5 figures, 6 tables, 49 reference

OMAE2006-92116 TIME-VARIANT RELIABILITY ASSESSMENT OF FPSO CONSIDERING CORROSION AND COLLISION

ABSTRACT Floating production, storage, and offloading (FPSO) system has been widely used in the offshore oil and gas exploitations. Since it has long intervals of docking for thorough inspection and maintenance, and is exposed to collision risk at sea, the time-variant reliability of FPSO becomes very important as for the risks of corrosion and collision

Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily Discriminating

Unsupervised graph representation learning (UGRL) has drawn increasing research attention and achieved promising results in several graph analytic tasks. Relying on the homophily assumption, existing UGRL methods tend to smooth the learned node representations along all edges, ignoring the existence of heterophilic edges that connect nodes with distinct attributes. As a result, current methods are hard to generalize to heterophilic graphs where dissimilar nodes are widely connected, and also vulnerable to adversarial attacks. To address this issue, we propose a novel unsupervised Graph Representation learning method with Edge hEterophily discriminaTing (GREET) which learns representations by discriminating and leveraging homophilic edges and heterophilic edges. To distinguish two types of edges, we build an edge discriminator that infers edge homophily/heterophily from feature and structure information. We train the edge discriminator in an unsupervised way through minimizing the crafted pivot-anchored ranking loss, with randomly sampled node pairs acting as pivots. Node representations are learned through contrasting the dual-channel encodings obtained from the discriminated homophilic and heterophilic edges. With an effective interplaying scheme, edge discriminating and representation learning can mutually boost each other during the training phase. We conducted extensive experiments on 14 benchmark datasets and multiple learning scenarios to demonstrate the superiority of GREET.Comment: 14 pages, 7 tables, 6 figures, accepted by AAAI 202

Karate Club: An API Oriented Open-Source Python Framework for Unsupervised Learning on Graphs

We present Karate Club a Python framework combining more than 30 state-of-the-art graph mining algorithms which can solve unsupervised machine learning tasks. The primary goal of the package is to make community detection, node and whole graph embedding available to a wide audience of machine learning researchers and practitioners. We designed Karate Club with an emphasis on a consistent application interface, scalability, ease of use, sensible out of the box model behaviour, standardized dataset ingestion, and output generation. This paper discusses the design principles behind this framework with practical examples. We show Karate Club's efficiency with respect to learning performance on a wide range of real world clustering problems, classification tasks and support evidence with regards to its competitive speed.Comment: The frameworks is available at: https://github.com/benedekrozemberczki/karateclu

Characteristic Functions on Graphs: Birds of a Feather, from Statistical Descriptors to Parametric Models

In this paper, we propose a flexible notion of characteristic functions defined on graph vertices to describe the distribution of vertex features at multiple scales. We introduce FEATHER, a computationally efficient algorithm to calculate a specific variant of these characteristic functions where the probability weights of the characteristic function are defined as the transition probabilities of random walks. We argue that features extracted by this procedure are useful for node level machine learning tasks. We discuss the pooling of these node representations, resulting in compact descriptors of graphs that can serve as features for graph classification algorithms. We analytically prove that FEATHER describes isomorphic graphs with the same representation and exhibits robustness to data corruption. Using the node feature characteristic functions we define parametric models where evaluation points of the functions are learned parameters of supervised classifiers. Experiments on real world large datasets show that our proposed algorithm creates high quality representations, performs transfer learning efficiently, exhibits robustness to hyperparameter changes, and scales linearly with the input size.Comment: Source code is available at: https://github.com/benedekrozemberczki/FEATHE

Link Prediction with Contextualized Self-Supervision

Link prediction aims to infer the existence of a link between two nodes in a network. Despite their wide application, the success of traditional link prediction algorithms is hindered by three major challenges -- link sparsity, node attribute noise and network dynamics -- that are faced by real-world networks. To overcome these challenges, we propose a Contextualized Self-Supervised Learning (CSSL) framework that fully exploits structural context prediction for link prediction. The proposed CSSL framework forms edge embeddings through aggregating pairs of node embeddings constructed via a transformation on node attributes, which are used to predict the link existence probability. To generate node embeddings tailored for link prediction, structural context prediction is leveraged as a self-supervised learning task to boost link prediction. Two types of structural contexts are investigated, i.e., context nodes collected from random walks vs. context subgraphs. The CSSL framework can be trained in an end-to-end manner, with the learning of node and edge embeddings supervised by link prediction and the self-supervised learning task. The proposed CSSL is a generic and flexible framework in the sense that it can handle both transductive and inductive link prediction settings, and both attributed and non-attributed networks. Extensive experiments and ablation studies on seven real-world benchmark graph datasets demonstrate the superior performance of the proposed self-supervision based link prediction algorithm over state-of-the-art baselines on different types of networks under both transductive and inductive settings. The proposed CSSL also yields competitive performance in terms of its robustness to node attribute noise and scalability over large-scale networks

DYNAMIC CHARACTERISTICS ANALYSIS OF FLEXIBLE FOUNDATION VIBRATION ISOLATION SYSTEM

A general coupled dynamic model of the isolation system is established,which is composed of isolation object,nonlinear isolators and flexible foundation. By transforming the system dynamic equation into an expression of initial value problem of differential equation,the calculation method of transmitted power flow is deduced,which is put forward to evaluation and analysis of isolation effectiveness. Based on an example of vibration isolation system of a small unmanned aircraft engine,the Runge-Kutta method is applied to simulation and estimation of power flow transmitted through nonlinear isolators with hypothetical different stiffness characteristics. It is presented that properly designed nonlinear isolators such as endowed with paralleled negative stiffness or piecewise linear stiffness could effectively reduce the power flow transmission of isolation system