GOTHAM: Graph Class Incremental Learning Framework under Weak Supervision

• URL: http://arxiv.org/abs/2504.04954v1
• Date: Mon, 07 Apr 2025 11:39:13 GMT
• Title: GOTHAM: Graph Class Incremental Learning Framework under Weak Supervision
• Authors: Aditya Hemant Shahane, Prathosh A. P, Sandeep Kumar,
• Abstract summary: In graph analysis, unlabeled nodes are categorized into predefined classes.<n>Traditional methods assume abundant labeled data for training, which isn't always feasible.<n>We propose emphGraph Class Incremental Learning under Weak Supervision (GCL), addressing this challenge by meta-training on base classes with limited labeled instances.
• Score: 6.886009879500963
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Graphs are growing rapidly, along with the number of distinct label categories associated with them. Applications like e-commerce, healthcare, recommendation systems, and various social media platforms are rapidly moving towards graph representation of data due to their ability to capture both structural and attribute information. One crucial task in graph analysis is node classification, where unlabeled nodes are categorized into predefined classes. In practice, novel classes appear incrementally sometimes with just a few labels (seen classes) or even without any labels (unseen classes), either because they are new or haven't been explored much. Traditional methods assume abundant labeled data for training, which isn't always feasible. We investigate a broader objective: \emph{Graph Class Incremental Learning under Weak Supervision (GCL)}, addressing this challenge by meta-training on base classes with limited labeled instances. During the incremental streams, novel classes can have few-shot or zero-shot representation. Our proposed framework GOTHAM efficiently accommodates these unlabeled nodes by finding the closest prototype representation, serving as class representatives in the attribute space. For Text-Attributed Graphs (TAGs), our framework additionally incorporates semantic information to enhance the representation. By employing teacher-student knowledge distillation to mitigate forgetting, GOTHAM achieves promising results across various tasks. Experiments on datasets such as Cora-ML, Amazon, and OBGN-Arxiv showcase the effectiveness of our approach in handling evolving graph data under limited supervision. The repository is available here: \href{https://github.com/adityashahane10/GOTHAM--Graph-based-Class-Incremental-Learning-Framework-under-We ak-Supervision}{\small \textcolor{blue}{Code}}

Related papers

• LLM as GNN: Graph Vocabulary Learning for Text-Attributed Graph Foundation Models [54.82915844507371]
  Text-Attributed Graphs (TAGs) are ubiquitous in real-world scenarios.<n>Despite large efforts to integrate Large Language Models (LLMs) and Graph Neural Networks (GNNs) for TAGs, existing approaches suffer from decoupled architectures.<n>We propose PromptGFM, a versatile GFM for TAGs grounded in graph vocabulary learning.
  arXiv  Detail & Related papers  (2025-03-05T09:45:22Z)
• GraphCLIP: Enhancing Transferability in Graph Foundation Models for Text-Attributed Graphs [27.169892145194638]
  GraphCLIP is a framework to learn graph foundation models with strong cross-domain zero/few-shot transferability.<n>We generate and curate large-scale graph-summary pair data with the assistance of LLMs.<n>For few-shot learning, we propose a novel graph prompt tuning technique aligned with our pretraining objective.
  arXiv  Detail & Related papers  (2024-10-14T09:40:52Z)
• ConGraT: Self-Supervised Contrastive Pretraining for Joint Graph and Text Embeddings [20.25180279903009]
  We propose Contrastive Graph-Text pretraining (ConGraT) for jointly learning separate representations of texts and nodes in a text-attributed graph (TAG) Our method trains a language model (LM) and a graph neural network (GNN) to align their representations in a common latent space using a batch-wise contrastive learning objective inspired by CLIP. Experiments demonstrate that ConGraT outperforms baselines on various downstream tasks, including node and text category classification, link prediction, and language modeling.
  arXiv  Detail & Related papers  (2023-05-23T17:53:30Z)
• Semi-Supervised Hierarchical Graph Classification [54.25165160435073]
  We study the node classification problem in the hierarchical graph where a 'node' is a graph instance. We propose the Hierarchical Graph Mutual Information (HGMI) and present a way to compute HGMI with theoretical guarantee. We demonstrate the effectiveness of this hierarchical graph modeling and the proposed SEAL-CI method on text and social network data.
  arXiv  Detail & Related papers  (2022-06-11T04:05:29Z)
• DOTIN: Dropping Task-Irrelevant Nodes for GNNs [119.17997089267124]
  Recent graph learning approaches have introduced the pooling strategy to reduce the size of graphs for learning. We design a new approach called DOTIN (underlineDrunderlineopping underlineTask-underlineIrrelevant underlineNodes) to reduce the size of graphs. Our method speeds up GAT by about 50% on graph-level tasks including graph classification and graph edit distance.
  arXiv  Detail & Related papers  (2022-04-28T12:00:39Z)
• Neural Graph Matching for Pre-training Graph Neural Networks [72.32801428070749]
  Graph neural networks (GNNs) have been shown powerful capacity at modeling structural data. We present a novel Graph Matching based GNN Pre-Training framework, called GMPT. The proposed method can be applied to fully self-supervised pre-training and coarse-grained supervised pre-training.
  arXiv  Detail & Related papers  (2022-03-03T09:53:53Z)
• Graph Few-shot Class-incremental Learning [25.94168397283495]
  The ability to incrementally learn new classes is vital to all real-world artificial intelligence systems. In this paper, we investigate the challenging yet practical problem, Graph Few-shot Class-incremental (Graph FCL) problem. We put forward a Graph Pseudo Incremental Learning paradigm by sampling tasks recurrently from the base classes. We present a task-sensitive regularizer calculated from task-level attention and node class prototypes to mitigate overfitting onto either novel or base classes.
  arXiv  Detail & Related papers  (2021-12-23T19:46:07Z)
• Knowledge-Guided Multi-Label Few-Shot Learning for General Image Recognition [75.44233392355711]
  KGGR framework exploits prior knowledge of statistical label correlations with deep neural networks. It first builds a structured knowledge graph to correlate different labels based on statistical label co-occurrence. Then, it introduces the label semantics to guide learning semantic-specific features. It exploits a graph propagation network to explore graph node interactions.
  arXiv  Detail & Related papers  (2020-09-20T15:05:29Z)
• Inverse Graph Identification: Can We Identify Node Labels Given Graph Labels? [89.13567439679709]
  Graph Identification (GI) has long been researched in graph learning and is essential in certain applications. This paper defines a novel problem dubbed Inverse Graph Identification (IGI) We propose a simple yet effective method that makes the node-level message passing process using Graph Attention Network (GAT) under the protocol of GI.
  arXiv  Detail & Related papers  (2020-07-12T12:06:17Z)
• Adaptive-Step Graph Meta-Learner for Few-Shot Graph Classification [25.883839335786025]
  We propose a novel framework consisting of a graph meta-learner, which uses GNNs based modules for fast adaptation on graph data. Our framework gets state-of-the-art results on several few-shot graph classification tasks compared to baselines.
  arXiv  Detail & Related papers  (2020-03-18T14:38:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.