How can we accurately classify graphs using only positive and unlabeled instances? This setting commonly arises in real-world tasks. For example, in drug side effect prediction, each drug is represented as a graph based on its chemical structure, and only those observed to cause adverse effects are labeled as positive. The remaining drugs are left unlabeled—not because they are confirmed safe, but because such side effects have not yet been discovered or reported. A challenge is that positive and negative graphs often share similar substructures, making it difficult for models to capture the subtle structural differences that distinguish true positives among unlabeled ones. This difficulty is further exacerbated by the absence of explicit negative labels, which leaves the model without clear guidance on how to separate informative from non-informative patterns. We propose Delta-PU, an accurate method for PU graph classification. Delta-PU performs dual reweighting: hop-level reweighting to emphasize informative substructures during pooling, and graph-level reweighting to suppress ambiguous unlabeled graphs. This dual mechanism allows the model to progressively focus on discriminative patterns while reducing the uncertainty introduced by unlabeled instances. Extensive experiments demonstrate that Delta-PU achieves the state-of-the-art performance in PU graph classification.

Many real-world datasets including stock prices or disease records are represented as regular or irregular tensors across multiple domains.How can we accurately capture patterns from both irregular and regular tensors in a newly emerging domain by leveraging existing ones from multiple domains?This problem is crucial for applications such as identifying patterns of new diseases using data from existing ones. A main challenge is that the new target tensors contain limited information due to their recent emergence. Previously, PARAFAC2- and PARAFAC-based methods have been widely used to find patterns in irregular and regular tensors, respectively, through decomposing them into latent factors. However, they cannot effectively transfer knowledge from previously known tensors to the new one. In this work, we propose a fast and accurate domain adaptation method for tensor decomposition. We proposeMeta-P2for irregular tensors andMeta-Pfor regular tensors. BothMeta-P2andMeta-Plearn general and easily-adaptable information– - referred to as the meta factor—from multiple source domains. Using this meta factor, they efficiently identify patterns in a new target tensor. Extensive experiments on real-world datasets show thatMeta-P2andMeta-Pachieve the state-of-the-art performance across various downstream tasks, including missing value prediction and anomaly detection.

How can we accurately predict the signs of unseen links in partially observed signed graphs? Signed graphs are widely used to represent complex relationships in areas such as social and biological networks. Although prior methods enhance representation learning by extending Graph Neural Networks with social theories, they rely on the unrealistic assumption that all link signs are known. In practice, however, link signs are often only partially labeled due to the high cost or difficulty of obtaining ground-truth annotations. For example, in the Bitcoin transaction network, only a subset of interactions can be labeled as trusted or untrusted, while many remain unlabeled. In this work, we propose PIGLET (PROBABILISTIC MESSAGE PASSING FOR SEMI-SUPERVISED LINK SIGN PREDICTION), a novel approach for accurate link sign prediction on signed graphs with partially observed sign labels. The main idea is to probabilistically interpret unlabeled links as both positive and negative based on a soft-labeling strategy, allowing information to be propagated through both types of edges. To mitigate the effect of uncertain predictions, we use confidence-based weights to downplay unreliable edges. PIGLET balances the importance of edges with relation-aware attention scores as well. PIGLET establishes a theoretical connection between the ExpectationMaximization (EM) algorithm and its iterative refinement of node embeddings and soft labels. Extensive experiments show that PIGLET consistently outperforms existing methods on realworld datasets under semi-supervised setting.

Given an irregular tensor from a newly emerging domain, how can we quickly and accurately capture its patterns utilizing existing irregular tensors in multiple domains? The problem is of great importance for various tasks such as finding patterns of a new disease using pre-existing diseases data. This is challenging as new target tensors have limited information due to their recent emergence. Thus, carefully utilizing the existing source tensors for analyzing the target tensor is helpful. PARAFAC2 decomposition is a strong tool for finding the patterns of irregular tensors, and the patterns are used in many applications such as missing value prediction and anomaly detection. However, previous PARAFAC2-based works cannot adaptably handle newly emerging target tensors utilizing the source tensors.

How can we classify graph-structured data only with positive labels? Graph-based positive-unlabeled (PU) learning is to train a binary classifier given only the positive labels when the relationship between examples is given as a graph. The problem is of great importance for various tasks such as detecting malicious accounts in a social network, which are difficult to be modeled by supervised learning when the true negative labels are absent. Previous works for graph-based PU learning assume that the prior distribution of positive nodes is known in advance, which is not true in many real-world cases. In this work, we propose GRAB (Graph-based Risk minimization with iterAtive Belief propagation), a novel end-to-end approach for graph-based PU learning that requires no class prior. GRAB models a given graph as a Markov network and runs the marginalization and update steps iteratively. The marginalization step estimates the marginals of latent variables, while the update step trains a classifier network utilizing the computed priors in the objective function. Extensive experiments on five datasets show that GRAB achieves state-of-the-art accuracy, even compared with previous methods that are given the true prior.