Indian Institute of Art & Design

Large graphs are becoming ubiquitous, presenting significant computational hurdles in data processing and analysis. Graph Coarsening algorithms are frequently employed to condense large graphs while preserving key graph properties. Real-world graphs also have features or contexts associated with each node. However, existing coarsening methods often overlook simultaneity across node features and structural information. Recent approaches to alleviate this limitation are computationally intensive, and primarily suited for homophilic datasets. Most existing approaches are unsuitable for streaming and evolving graphs, as they require recomputation of the coarsened graph at every timestamp. In this paper, we introduce a Fast and Scalable Hashing-Based Universal Graph Coarsening (UGC) Framework, that integrates locality-sensitive hashing, and feature augmentation to effectively coarsen graphs. UGC is exceptionally fast, straightforward to implement, and capable of handling homophilic, heterophilic, and streaming graphs making it a truly universal solution for graph coarsening. We use an optimization-based framework to minimize a constrained $\epsilon$ similarity between the original and coarsened graphs, where $\epsilon$ is between zero and one. Through extensive experimentation on real and synthetic datasets, we demonstrate the effectiveness of our approach in terms of improved runtime complexity and generalization to heterophilic and streaming graphs. Furthermore, we showcase its utility in downstream tasks, emphasizing its scalability for training graph neural networks on coarsened graphs from benchmark real-world datasets.

Link prediction is a pivotal task in graph mining with wide-ranging applications in social networks, recommendation systems, and knowledge graph completion. However, many leading Graph Neural Network (GNN) models often neglect the valuable semantic information aggregated at the class level. To address this limitation, this paper introduces CGLE (Classlabel Graph Link Estimator), a novel framework designed to augment GNN-based link prediction models. CGLE operates by constructing a class-conditioned link probability matrix, where each entry represents the probability of a link forming between two node classes. This matrix is derived from either available ground-truth labels or from pseudo-labels obtained through clustering. The resulting class-based prior is then concatenated with the structural link embedding from a backbone GNN, and the combined representation is processed by a Multi-Layer Perceptron (MLP) for the final prediction. Crucially, CGLE's logic is encapsulated in an efficient preprocessing stage, leaving the computational complexity of the underlying GNN model unaffected. We validate our approach through extensive experiments on a broad suite of benchmark datasets, covering both homophilous and sparse heterophilous graphs. The results show that CGLE yields substantial performance gains over strong baselines like NCN/NCNC, with improvements in HR@100 of over 10% points on homophilous datasets like Pubmed and DBLP. On the sparse heterophilous graphs, CGLE delivers an MRR improvement of over 4% on the Chameleon dataset. Our work underscores the efficacy of integrating global, data-driven semantic priors, presenting a compelling alternative to the pursuit of ever-morecomplex model architectures. Code to reproduce our findings is available at https://github.com/data-iitd/cgle-icdm2025.

The studies have shown that research is fundamental to design education and to equip students with the nuances of research, research methodology and academic paper writing are included in the curriculum. The purpose of this study is to establish that teaching research skills, rather than research methods, is a more engaged learning process for foundation-level design students at the undergraduate level. Teaching strategies applied in a controlled environment built on Bloom’s taxonomy and Vygotsky’s zone of proximal development theory using Kolb’s experiential learning theory along with motivational strategies have proven to be engaging and enabling for the students. This is mapped through a cultural context-specific research project. To analyze and evaluate the effectiveness of active teaching and learning strategies applied in the entire module, a student survey was conducted on two consecutive batches. According to our findings along with achieving all the intended learning outcomes, the strategies have been instrumental in increasing students’ engagement and minimize their inhibitions and have provided opportunities to keep them intrinsically and extrinsically motivated toward practicing research skills.

There is a widespread view that inflation or inflationary expectations are the only way to overcome the zero lower bound on interest rate. This article shows that this is not true. It is shown that a tax-subsidy scheme can be used to overcome the zero lower bound on interest rate—without any inflation or inflationary expectations. A simple ‘old’ Keynesian model is used though the treatment is novel and in-depth. The article ends with a discussion that suggests that the ‘non-inflation solution’ is superior to the ‘inflation solution’ under some conditions. JEL Classification: E31m E43, E63. The difficulty lies, not in the new ideas, but in escaping from the old ones … . (John Maynard Keynes, 1936)

Rechtswissenschaft und Rechtsliteratur im 20. Jahrhundert , Seite 1019 - 1026