There has been a recent surge of interest in designing Graph Neural Networks (GNNs) for semi-supervised learning tasks. Unfortunately this work has assumed that the nodes labeled for use in training were selected uniformly at random (i.e. are an IID sample). However in many real world scenarios gathering labels for graph nodes is both expensive and inherently biased -- so this assumption can not be met. GNNs can suffer poor generalization when this occurs, by overfitting to superfluous regularities present in the training data. In this work we present a method, Shift-Robust GNN (SR-GNN), designed to account for distributional differences between biased training data and the graph's true inference distribution. SR-GNN adapts GNN models for the presence of distributional shifts between the nodes which have had labels provided for training and the rest of the dataset. We illustrate the effectiveness of SR-GNN in a variety of experiments with biased training datasets on common GNN benchmark datasets for semi-supervised learning, where we see that SR-GNN outperforms other GNN baselines by accuracy, eliminating at least (~40%) of the negative effects introduced by biased training data. On the largest dataset we consider, ogb-arxiv, we observe an 2% absolute improvement over the baseline and reduce 30% of the negative effects.

There has been a recent surge of interest in designing Graph Neural Networks (GNNs) for semi-supervised learning tasks. Unfortunately this work has assumed that the nodes labeled for use in training were selected uniformly at random (i.e. are an IID sample). However in many real world scenarios gathering labels for graph nodes is both expensive and inherently biased -- so this assumption can not be met. GNNs can suffer poor generalization when this occurs, by overfitting to superfluous regularities present in the training data. In this work we present a method, Shift-Robust GNN (SR-GNN), designed to account for distributional differences between biased training data and the graph's true inference distribution.

Session-based recommendation systems aim to model users' interests based on their sequential interactions to predict the next item in an ongoing session. In this work, we present a novel approach that can be used in session-based recommendations (SBRs). Our goal is to enhance the prediction accuracy of an existing session-based recommendation model, the SR-GNN model, by introducing an adaptive weighting mechanism applied to the graph neural network (GNN) vectors. This mechanism is designed to incorporate various types of side information obtained through different methods during the study. Items are assigned varying degrees of importance within each session as a result of the weighting mechanism. We hypothesize that this adaptive weighting strategy will contribute to more accurate predictions and thus improve the overall performance of SBRs in different scenarios. The adaptive weighting strategy can be utilized to address the cold start problem in SBRs by dynamically adjusting the importance of items in each session, thus providing better recommendations in cold start situations, such as for new users or newly added items. Our experimental evaluations on the Dressipi dataset demonstrate the effectiveness of the proposed approach compared to traditional models in enhancing the user experience and highlighting its potential to optimize the recommendation results in real-world applications.

Session-based recommender systems typically focus on using only the triplet (user_id, timestamp, item_id) to make predictions of users' next actions. In this paper, we aim to utilize side information to help recommender systems catch patterns and signals otherwise undetectable. Specifically, we propose a general framework for incorporating item-specific side information into the recommender system to enhance its performance without much modification on the original model architecture. Experimental results on several models and datasets prove that with side information, our recommender system outperforms state-of-the-art models by a considerable margin and converges much faster. Additionally, we propose a new type of loss to regularize the attention mechanism used by recommender systems and evaluate its influence on model performance. Furthermore, through analysis, we put forward a few insights on potential further improvements.

Session-based recommendation (SBR) problem, which focuses on next-item prediction for anonymous users, has received increasingly more attention from researchers. Existing graph-based SBR methods all lack the ability to differentiate between sessions with the same last item, and suffer from severe popularity bias. Inspired by nowadays emerging contrastive learning methods, this paper presents a Simple Contrastive Graph Neural Network for Session-based Recommendation (SimCGNN). In SimCGNN, we first obtain normalized session embeddings on constructed session graphs. We next construct positive and negative samples of the sessions by two forward propagation and a novel negative sample selection strategy, and then calculate the constructive loss. Finally, session embeddings are used to give prediction. Extensive experiments conducted on two real-word datasets show our SimCGNN achieves a significant improvement over state-of-the-art methods.

Stability of graph neural networks (GNNs) characterizes how GNNs react to graph perturbations and provides guarantees for architecture performance in noisy scenarios. This paper develops a self-regularized graph neural network (SR-GNN) solution that improves the architecture stability by regularizing the filter frequency responses in the graph spectral domain. The SR-GNN considers not only the graph signal as input but also the eigenvectors of the underlying graph, where the signal is processed to generate task-relevant features and the eigenvectors to characterize the frequency responses at each layer. We train the SR-GNN by minimizing the cost function and regularizing the maximal frequency response close to one. The former improves the architecture performance, while the latter tightens the perturbation stability and alleviates the information loss through multi-layer propagation. We further show the SR-GNN preserves the permutation equivariance, which allows to explore the internal symmetries of graph signals and to exhibit transference on similar graph structures. Numerical results with source localization and movie recommendation corroborate our findings and show the SR-GNN yields a comparable performance with the vanilla GNN on the unperturbed graph but improves substantially the stability.

Abstract--Over the past few years, a significant progress has been made in deep convolutional neural networks (CNNs)-based image recognition. This is mainly due to the strong ability of such networks in mining discriminative object pose and parts information from texture and shape. This is often inappropriate for fine-grained visual classification (FGVC) since it exhibits high intra-class and low inter-class variances due to occlusions, deformation, illuminations, etc. Thus, an expressive feature representation describing global structural information is a key to characterize an object/ scene. To this end, we propose a method that effectively captures subtle changes by aggregating contextaware features from most relevant image-regions and their importance in discriminating fine-grained categories avoiding the bounding-box and/or distinguishable part annotations. Our approach is inspired by the recent advancement in self-attention and graph neural networks (GNNs) approaches to include a simple yet effective relation-aware feature transformation and its refinement using a context-aware attention mechanism to boost the discriminability of the transformed feature in an end-to-end learning process. Our model is evaluated on eight benchmark datasets consisting of fine-grained objects and human-object interactions. For clarity, 4 different regions are shown here. A HE advent of deep convolutional neural networks (CNN) has significantly enhanced image recognition performance key step to address this challenge is to extract discriminating in the past decade. It is achieved mainly due to their features from vital object-parts and combine them for the abilities to provide a high-level description (e.g., global shape representation of a consistent distinctive global structure of a and appearance) of image content by capturing discriminative given class. The current state-of-the-art (SotA) approaches are object-pose and -parts information from texture and shape. We refer the interested readers to [6] for a their performance in solving fine-grained visual classification detailed survey.

Existing methods for session-based recommendation can be summarized into several categories. The most well-known and probably the most general method is matrix factorization. Matrix factorization is to factorize a user-item rating matrix into two low-rank matrices each representing latent factors of users and items. Also, there are some item-based neighborhood methods that count the co-occurrence of items in the same session. Markov chain methods can account for the sequential nature of data, but they make a strong assumption that the sequence components are independent.