Reviewerly/exHarmony huggingface dataset:
Multi-source Inductive Knowledge Graph Transfer

Large-scale information systems, such as knowledge graphs (KGs), enterprise system networks, often exhibit dynamic and complex activities. Recent research has shown that formalizing these information systems as graphs can effectively characterize the entities (nodes) and their relationships (edges). Transferring knowledge from existing well-curated source graphs can help construct the target graph of newly-deployed systems faster and better which no doubt will benefit downstream tasks such as link prediction and anomaly detection for new systems. However, current graph transferring methods are either based on a single source, which does not sufficiently consider multiple available sources, or not selectively learns from these sources. In this paper, we propose MSGT-GNN, a graph knowledge transfer model for efficient graph link prediction from multiple source graphs. MSGT-GNN consists of two components: the Intra-Graph Encoder, which embeds latent graph features of system entities into vectors; and the graph transferor, which utilizes graph attention mechanism to learn and optimize the embeddings of corresponding entities from multiple source graphs, in both node level and graph level. Experimental results on multiple real-world datasets from various domains show that MSGT-GNN outperforms other baseline approaches in the link prediction and demonstrate the merit of attentive graph knowledge transfer and the effectiveness of MSGT-GNN.
RDF Knowledge Base Summarization by Inducing First-Order Horn Rules

RDF knowledge base summarization produces a compact and faithful abstraction for entities, relations, and ontologies. The summary is critical to a wide range of knowledge-based applications, such as query answering and KB indexing. The patterns of graph structure and/or association are commonly employed to summarize and reduce the number of triples. However, knowledge coverage is low in state-of-the-art techniques due to limited expressiveness of patterns, where variables are under-explored to capture matched arguments in relations. This paper proposes a novel summarization technique based on first-order logic rules where quantified variables are extensively taken into account. We formalize this new summarization problem to illustrate how the rules are used to replace triples. The top-down rule mining is also improved to maximize the reusability of cached results. Qualitative and quantitative analyses are comprehensively done by comparing our technique against state-of-the-art tools, with showing that our approach outperforms the rivals in conciseness, completeness, and performance.
Self-supervised Graph Learning with Segmented Graph Channels

Self-supervised graph learning adopts self-defined signals as supervision to learn representations. This learning paradigm solves the critical problem of utilizing unlabeled graph data. Conventional self-supervised graph learning methods rely on graph data augmentation to generate different views of the input data as self-defined signals. However, the views generated by such an approach contain amounts of identical node features, which leads to the learning of redundant information. To this end, we propose Self-Supervised Graph Learning with Segmented Graph Channels (SGL-SGC) to address the issue. SGL-SGC divides the input graph data across the feature dimensions as Segmented Graph Channels (SGCs). By combining SGCs with data augmentation, SGL-SGC can generate views that vastly reduce the redundant information. We further design a feature-level weight-sensitive loss to jointly accelerate optimization and avoid the model falling into a local optimum. Empirically, the experiments on multiple benchmark datasets demonstrate that SGL-SGC outperforms the state-of-the-art methods in contrastive graph learning tasks. Ablation studies verify the effectiveness and efficiency of different parts of SGL-SGC.
An Ion Exchange Mechanism Inspired Story Ending Generator for Different Characters

Story ending generation aims at generating reasonable endings for a given story context. Most existing studies in this area focus on generating coherent or diversified story endings, while they ignore that different characters may lead to different endings for a given story. In this paper, we propose a Character-oriented Story Ending Generator (CoSEG) to customize an ending for each character in a story. Specifically, we first propose a character modeling module to learn the personalities of characters from their descriptive experiences extracted from the story context. Then, inspired by the ion exchange mechanism in chemical reactions, we design a novel vector breaking/forming module to learn the intrinsic interactions between each character and the corresponding context through an analogical information exchange procedure. Finally, we leverage the attention mechanism to learn effective character-specific interactions and feed each interaction into a decoder to generate character-orient endings. Extensive experimental results and case studies demonstrate that CoSEG achieves significant improvements in the quality of generated endings compared with state-of-the-art methods, and it effectively customizes the endings for different characters.
Contextual Information and Commonsense Based Prompt for Emotion Recognition in Conversation

Emotion recognition in conversation (ERC) aims to detect the emotion for each utterance in a given conversation. The newly proposed ERC models have leveraged pre-trained language models (PLMs) with the paradigm of pre-training and fine-tuning to obtain good performance. However, these models seldom exploit PLMs’ advantages thoroughly, and perform poorly for the conversations lacking explicit emotional expressions. In order to fully leverage the latent knowledge related to the emotional expressions in utterances, we propose a novel ERC model CISPER with the new paradigm of prompt and language model (LM) tuning. Specifically, CISPER is equipped with the prompt blending the contextual information and commonsense related to the interlocutor’s utterances, to achieve ERC more effectively. Our extensive experiments demonstrate CISPER’s superior performance over the state-of-the-art ERC models, and the effectiveness of leveraging these two kinds of significant prompt information for performance gains. To reproduce our experimental results conveniently, CISPER’s source code and the datasets have been shared at https://github.com/DeqingYang/CISPER .
Edge but not Least: Cross-View Graph Pooling

Graph neural networks have emerged as a powerful representation learning model for undertaking various graph prediction tasks. Various graph pooling methods have been developed to coarsen an input graph into a succinct graph-level representation through aggregating node embeddings obtained via graph convolution. However, because most graph pooling methods are heavily node-centric, they fail to fully leverage the crucial information contained in graph structure. This paper presents a cross-view graph pooling method (Co-Pooling) that explicitly exploits crucial graph substructures for learning graph representations. Co-Pooling is designed to fuse the pooled representations from both node view and edge view. Through cross-view interaction, edge-view pooling and node-view pooling mutually reinforce each other to learn informative graph representations. Extensive experiments on one synthetic and 15 real-world graph datasets validate the effectiveness of our Co-Pooling method. Our results and analysis show that (1) our method is able to yield promising results over graphs with various types of node attributes, and (2) our method can achieve superior performance over state-of-the-art pooling methods on graph classification and regression tasks.
Submodular Meta Data Compiling for Meta Optimization

The search for good hyper-parameters is crucial for various deep learning methods. In addition to the hyper-parameter tuning on validation data, meta-learning provides a promising manner for optimizing the hyper-parameters, referred to as meta optimization. In all existing meta optimization methods, the meta data set is directly given or constructed from training data based on simple selection criteria. This study investigates the automatic compiling of a high-quality meta set from training data with more well-designed criteria and the submodular optimization strategy. First, a theoretical analysis is conducted for the generalization gap of meta optimization with a general meta data compiling method. Illuminated by the theoretical analysis, four criteria are presented to reduce the gap’s upper bound. Second, the four criteria are cooperated to construct an optimization problem for the automatic meta data selection from training data. The optimization problem is proven to be submodular, and the submodular optimization strategy is employed to optimize the selection process. An extensive experimental study is conducted, and results indicate that our compiled meta data can yield better or comparable performances than the data compiled with existing methods.
PrUE: Distilling Knowledge from Sparse Teacher Networks

Although deep neural networks have enjoyed remarkable success across a wide variety of tasks, their ever-increasing size also imposes significant overhead on deployment. To compress these models, knowledge distillation was proposed to transfer knowledge from a cumbersome (teacher) network into a lightweight (student) network. However, guidance from a teacher does not always improve the generalization of students, especially when the size gap between student and teacher is large. Previous works argued that it was due to the high certainty of the teacher, resulting in harder labels that were difficult to fit. To soften these labels, we present a pruning method termed Prediction Uncertainty Enlargement (PrUE) to simplify the teacher. Specifically, our method aims to decrease the teacher’s certainty about data, thereby generating soft predictions for students. We empirically investigate the effectiveness of the proposed method with experiments on CIFAR-10/100, Tiny-ImageNet, and ImageNet. Results indicate that student networks trained with sparse teachers achieve better performance. Besides, our method allows researchers to distill knowledge from deeper networks to improve students further. Our code is made public at: https://github.com/wangshaopu/prue .
Transforming PageRank into an Infinite-Depth Graph Neural Network

Popular graph neural networks are shallow models, despite the success of very deep architectures in other application domains of deep learning. This reduces the modeling capacity and leaves models unable to capture long-range relationships. The primary reason for the shallow design results from over-smoothing, which leads node states to become more similar with increased depth. We build on the close connection between GNNs and PageRank, for which personalized PageRank introduces the consideration of a personalization vector. Adopting this idea, we propose the Personalized PageRank Graph Neural Network (PPRGNN), which extends the graph convolutional network to an infinite-depth model that has a chance to reset the neighbor aggregation back to the initial state in each iteration. We introduce a nicely interpretable tweak to the chance of resetting and prove the convergence of our approach to a unique solution without placing any constraints, even when taking infinitely many neighbor aggregations. As in personalized PageRank, our result does not suffer from over-smoothing. While doing so, time complexity remains linear while we keep memory complexity constant, independently of the depth of the network, making it scale well to large graphs. We empirically show the effectiveness of our approach for various node and graph classification tasks. PPRGNN outperforms comparable methods in almost all cases. (Our code is available at: https://github.com/roth-andreas/pprgnn. )
Fine-Grained Bidirectional Attention-Based Generative Networks for Image-Text Matching

In this paper, we propose a method called BiKA (Bidirectional Knowledge-assisted embedding and Attention-based generation) for the task of image-text matching. It mainly improves the embedding ability of images and texts from two aspects: first, modality conversion, we build a bidirectional image and text generation network to explore the positive effect of mutual conversion between modalities on image-text feature embedding; then is relational dependency, we built a bidirectional graph convolutional neural network to establish the dependency between objects, introduce non-Euclidean data into image-text fine-grained matching to explore the positive effect of this dependency on fine-grained embedding of images and texts. Experiments on two public datasets show that the performance of our method is significantly improved compared to many state-of-the-art models.
Anonymity can Help Minority: A Novel Synthetic Data Over-Sampling Strategy on Multi-label Graphs

In many real-world networks (e.g., social networks), nodes are associated with multiple labels and node classes are imbalanced, that is, some classes have significantly fewer samples than others. However, the research problem of imbalanced multi-label graph node classification remains unexplored. This non-trivial task challenges existing graph neural networks (GNNs) because the majority class could dominate the loss functions of GNNs and result in overfitting to those majority class features and label correlations. On non-graph data, minority over-sampling methods (such as SMOTE and its variants) have been demonstrated to be effective for the imbalanced data classification problem. This study proposes and validates a new hypothesis with unlabeled data oversampling, which is meaningless for imbalanced non-graph data; however, feature propagation and topological interplay mechanisms between graph nodes can facilitate representation learning of imbalanced graphs. Furthermore, we determine empirically that ensemble data synthesis through the creation of virtual minority samples in the central region of a minority, and the generation of virtual unlabeled samples in the boundary region between a minority and majority is the best practice for the imbalanced multi-label graph node classification task. Our proposed novel data over-sampling framework is evaluated using multiple real-word network datasets, and it outperforms diverse, strong benchmark models by a large margin.
Safe Exploration Method for Reinforcement Learning Under Existence of Disturbance

Recent rapid developments in reinforcement learning algorithms have been giving us novel possibilities in many fields. However, due to their exploring property, we have to take the risk into consideration when we apply those algorithms to safety-critical problems especially in real environments. In this study, we deal with a safe exploration problem in reinforcement learning under the existence of disturbance. We define the safety during learning as satisfaction of the constraint conditions explicitly defined in terms of the state and propose a safe exploration method that uses partial prior knowledge of a controlled object and disturbance. The proposed method assures the satisfaction of the explicit state constraints with a pre-specified probability even if the controlled object is exposed to a stochastic disturbance following a normal distribution. As theoretical results, we introduce sufficient conditions to construct conservative inputs not containing an exploring aspect used in the proposed method and prove that the safety in the above explained sense is guaranteed with the proposed method. Furthermore, we illustrate the validity and effectiveness of the proposed method through numerical simulations of an inverted pendulum and a four-bar parallel link robot manipulator.
Team-Imitate-Synchronize for Solving Dec-POMDPs

Multi-agent collaboration under partial observability is a difficult task. Multi-agent reinforcement learning (MARL) algorithms that do not leverage a model of the environment struggle with tasks that require sequences of collaborative actions, while Dec-POMDP algorithms that use such models to compute near-optimal policies, scale poorly. In this paper, we suggest the Team-Imitate-Synchronize (TIS) approach, a heuristic, model-based method for solving such problems. Our approach begins by solving the joint team problem, assuming that observations are shared. Then, for each agent we solve a single agent problem designed to imitate its behavior within the team plan. Finally, we adjust the single agent policies for better synchronization. Our experiments demonstrate that our method provides comparable solutions to Dec-POMDP solvers over small problems, while scaling to much larger problems, and provides collaborative plans that MARL algorithms are unable to identify.
Understanding Difficulty-Based Sample Weighting with a Universal Difficulty Measure

Sample weighting is widely used in deep learning. A large number of weighting methods essentially utilize the learning difficulty of training samples to calculate their weights. In this study, this scheme is called difficulty-based weighting. Two important issues arise when explaining this scheme. First, a unified difficulty measure that can be theoretically guaranteed for training samples does not exist. The learning difficulties of the samples are determined by multiple factors including noise level, imbalance degree, margin, and uncertainty. Nevertheless, existing measures only consider a single factor or in part, but not in their entirety. Second, a comprehensive theoretical explanation is lacking with respect to demonstrating why difficulty-based weighting schemes are effective in deep learning. In this study, we theoretically prove that the generalization error of a sample can be used as a universal difficulty measure. Furthermore, we provide formal theoretical justifications on the role of difficulty-based weighting for deep learning, consequently revealing its positive influences on both the optimization dynamics and generalization performance of deep models, which is instructive to existing weighting schemes.
Noise-Efficient Learning of Differentially Private Partitioning Machine Ensembles

Differentially private decision tree algorithms have been popular since the introduction of differential privacy. While many private tree-based algorithms have been proposed for supervised learning tasks, such as classification, very few extend naturally to the semi-supervised setting. In this paper, we present a framework that takes advantage of unlabelled data to reduce the noise requirement in differentially private decision forests and improves their predictive performance. The main ingredients in our approach consist of a median splitting criterion that creates balanced leaves, a geometric privacy budget allocation technique, and a random sampling technique to compute the private splitting-point accurately. While similar ideas existed in isolation, their combination is new, and has several advantages: (1) The semi-supervised mode of operation comes for free. (2) Our framework is applicable in two different privacy settings: when label-privacy is required, and when privacy of the features is also required. (3) Empirical evidence on 18 UCI data sets and 3 synthetic data sets demonstrate that our algorithm achieves high utility performance compared to the current state of the art in both supervised and semi-supervised classification problems.
Non-IID Distributed Learning with Optimal Mixture Weights

Distributed learning can well solve the problem of training model with large-scale data, which has attracted much attention in recent years. However, most existing distributed learning algorithms set uniform mixture weights across clients when aggregating the global model, which impairs the accuracy under Non-IID (Not Independently or Identically Distributed) setting. In this paper, we present a general framework to optimize the mixture weights and show that our framework has lower expected loss than the uniform mixture weights framework theoretically. Moreover, we provide strong generalization guarantee for our framework, where the excess risk bound can converge at $$\mathcal {O}(1/n)$$ , which is as fast as centralized training. Motivated by the theoretical findings, we propose a novel algorithm to improve the performance of distributed learning under Non-IID setting. Through extensive experiments, we show that our algorithm outperforms other mainstream methods, which coincides with our theory.
Automatic Feature Engineering Through Monte Carlo Tree Search

The performance of machine learning models depends heavily on the feature space and feature engineering. Although neural networks have made significant progress in learning latent feature spaces from data, compositional feature engineering through nested feature transformations can reduce model complexity and can be particularly desirable for interpretability. To find suitable transformations automatically, state-of-the-art methods model the feature transformation space by graph structures and use heuristics such as $$\epsilon $$ -greedy to search for them. Such search strategies tend to become less efficient over time because they do not consider the sequential information of the candidate sequences and cannot dynamically adjust the heuristic strategy. To address these shortcomings, we propose a reinforcement learning-based automatic feature engineering method, which we call Monte Carlo tree search Automatic Feature Engineering (mCAFE). We employ a surrogate model that can capture the sequential information contained in the transformation sequence and thus can dynamically adjust the exploration strategy. It balances exploration and exploitation by Thompson sampling and uses a Long Short Term Memory (LSTM) based surrogate model to estimate sequences of promising transformations. In our experiments, mCAFE outperformed state-of-the-art automatic feature engineering methods on most common benchmark datasets.
A Scaling Law for Syn2real Transfer: How Much Is Your Pre-training Effective?

Synthetic-to-real transfer learning is a framework in which a synthetically generated dataset is used to pre-train a model to improve its performance on real vision tasks. The most significant advantage of using synthetic images is that the ground-truth labels are automatically available, enabling unlimited expansion of the data size without human cost. However, synthetic data may have a huge domain gap, in which case increasing the data size does not improve the performance. How can we know that? In this study, we derive a simple scaling law that predicts the performance from the amount of pre-training data. By estimating the parameters of the law, we can judge whether we should increase the data or change the setting of image synthesis. Further, we analyze the theory of transfer learning by considering learning dynamics and confirm that the derived generalization bound is consistent with our empirical findings. We empirically validated our scaling law on various experimental settings of benchmark tasks, model sizes, and complexities of synthetic images.