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Shubham Gupta

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11 papers

2 author rows

TMLR Journal 2026 Journal Article

Enhancing Deep Consistent Graph Metric with Affinity and Alignment for Incremental Social Event Detection using Cross-Layer Attention

Shraban Kumar Chatterjee
Shubham Gupta
Suman Kundu

Existing methods of event detection from social media (i.e., X), for instance, KPGNN, FinEvent, and CLKD, use triplet loss for feature separation. Triplet loss suffers from two notable discrepancies in the latent space: (i) inconsistency in intra-event and inter-event distances, and (ii) an inability to ensure the closeness of messages from the same event across different mini-batches. The present paper proposes two novel loss functions to improve consistency in the latent space. The first loss function guarantees consistent intra-event and inter-event distances by increasing the affinity between intra-event points. On the other hand, the alignment loss enhances the cosine similarity between the feature space and label space, thereby aligning features of the same event class across diverse mini-batches. We provide theoretical justification that the proposed loss ensures discriminative features in the latent space, like CGML, without its costly pairwise or specialised batching. Adding to our loss function, we introduce a new attention module designed to effectively address heterogeneous relations without necessitating a separate optimisation objective. Through comprehensive experimentation on two publicly available datasets, we have shown an average improvement of $24.05\%$, $27.23\%$ and $123.69\%$ in NMI, AMI and ARI, respectively, over supervised SOTA event detection methods. Our method also shows improvements over SOTA unsupervised event detection methods across both datasets. These are supported by statistical significance tests. Generalizability of the proposed loss in general clustering problem in graph domain is shown through experiments.

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ICML Conference 2025 Conference Paper

LAST SToP for Modeling Asynchronous Time Series

Shubham Gupta
Thibaut Durand
Graham W. Taylor
Lilian W. Bialokozowicz

We present a novel prompt design for Large Language Models (LLMs) tailored to Asynchronous Time Series. Unlike regular time series, which assume values at evenly spaced time points, asynchronous time series consist of timestamped events occurring at irregular intervals, each described in natural language. Our approach effectively utilizes the rich natural language of event descriptions, allowing LLMs to benefit from their broad world knowledge for reasoning across different domains and tasks. This allows us to extend the scope of asynchronous time series analysis beyond forecasting to include tasks like anomaly detection and data imputation. We further introduce Stochastic Soft Prompting, a novel prompt-tuning mechanism that significantly improves model performance, outperforming existing finetuning methods such as QLORA. Through extensive experiments on real-world datasets, we demonstrate that our approach achieves state-of-the-art performance across different tasks and datasets.

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ICML Conference 2023 Conference Paper

GRAFENNE: Learning on Graphs with Heterogeneous and Dynamic Feature Sets

Shubham Gupta
Sahil Manchanda
Sayan Ranu
Srikanta J. Bedathur

Graph neural networks (GNNs), in general, are built on the assumption of a static set of features characterizing each node in a graph. This assumption is often violated in practice. Existing methods partly address this issue through feature imputation. However, these techniques (i) assume uniformity of feature set across nodes, (ii) are transductive by nature, and (iii) fail to work when features are added or removed over time. In this work, we address these limitations through a novel GNN framework called GRAFENNE. GRAFENNE performs a novel allotropic transformation on the original graph, wherein the nodes and features are decoupled through a bipartite encoding. Through a carefully chosen message passing framework on the allotropic transformation, we make the model parameter size independent of the number of features and thereby inductive to both unseen nodes and features. We prove that GRAFENNE is at least as expressive as any of the existing message-passing GNNs in terms of Weisfeiler-Leman tests, and therefore, the additional inductivity to unseen features does not come at the cost of expressivity. In addition, as demonstrated over four real-world graphs, GRAFENNE empowers the underlying GNN with high empirical efficacy and the ability to learn in continual fashion over streaming feature sets.

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NeurIPS Conference 2022 Conference Paper

Consistency of Constrained Spectral Clustering under Graph Induced Fair Planted Partitions

Shubham Gupta
Ambedkar Dukkipati

Spectral clustering is popular among practitioners and theoreticians alike. While performance guarantees for spectral clustering are well understood, recent studies have focused on enforcing "fairness" in clusters, requiring them to be "balanced" with respect to a categorical sensitive node attribute (e. g. the race distribution in clusters must match the race distribution in the population). In this paper, we consider a setting where sensitive attributes indirectly manifest in an auxiliary representation graph rather than being directly observed. This graph specifies node pairs that can represent each other with respect to sensitive attributes and is observed in addition to the usual similarity graph. Our goal is to find clusters in the similarity graph while respecting a new individual-level fairness constraint encoded by the representation graph. We develop variants of unnormalized and normalized spectral clustering for this task and analyze their performance under a fair planted partition model induced by the representation graph. This model uses both the cluster membership of the nodes and the structure of the representation graph to generate random similarity graphs. To the best of our knowledge, these are the first consistency results for constrained spectral clustering under an individual-level fairness constraint. Numerical results corroborate our theoretical findings.

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NeSy Conference 2022 Conference Paper

Differentiable Rule Induction with Learned Relational Features

Remy Kusters
Yusik Kim
Marine Collery
Christian de Sainte Marie
Shubham Gupta

Rule-based decision models are attractive due to their interpretability. However, existing rule induction methods often result in long and consequently less interpretable rule models. This problem can often be attributed to the lack of appropriately expressive vocabulary, i. e. , relevant predicates used as literals in the decision model. Most existing rule induction algorithms presume pre-defined literals, naturally decoupling the definition of the literals from the rule learning phase. In contrast, we propose the Relational Rule Network (R2N), a neural architecture that learns literals that represent a linear relationship among numerical input features along with the rules that use them. This approach opens the door to increasing the expressiveness of induced decision models by coupling literal learning directly with rule learning in an end-to-end differentiable fashion. On benchmark tasks, we show that these learned literals are simple enough to retain interpretability, yet improve prediction accuracy and provide sets of rules that are more concise compared to state-of-the-art rule induction algorithms.

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ICML Conference 2022 Conference Paper

Optimal and Efficient Dynamic Regret Algorithms for Non-Stationary Dueling Bandits

Aadirupa Saha
Shubham Gupta

We study the problem of dynamic regret minimization in $K$-armed Dueling Bandits under non-stationary or time-varying preferences. This is an online learning setup where the agent chooses a pair of items at each round and observes only a relative binary ‘win-loss’ feedback for this pair sampled from an underlying preference matrix at that round. We first study the problem of static-regret minimization for adversarial preference sequences and design an efficient algorithm with $O(\sqrt{KT})$ regret bound. We next use similar algorithmic ideas to propose an efficient and provably optimal algorithm for dynamic-regret minimization under two notions of non-stationarities. In particular, we show $\tO(\sqrt{SKT})$ and $\tO({V_T^{1/3}K^{1/3}T^{2/3}})$ dynamic-regret guarantees, respectively, with $S$ being the total number of ‘effective-switches’ in the underlying preference relations and $V_T$ being a measure of ‘continuous-variation’ non-stationarity. These rates are provably optimal as justified with matching lower bound guarantees. Moreover, our proposed algorithms are flexible as they can be easily ‘blackboxed’ to yield dynamic regret guarantees for other notions of dueling bandits regret, including condorcet regret, best-response bounds, and Borda regret. The complexity of these problems have not been studied prior to this work despite the practicality of non-stationary environments. Our results are corroborated with extensive simulations.

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AAAI Conference 2022 Conference Paper

TIGGER: Scalable Generative Modelling for Temporal Interaction Graphs

Shubham Gupta
Sahil Manchanda
Srikanta Bedathur
Sayan Ranu

There has been a recent surge in learning generative models for graphs. While impressive progress has been made on static graphs, work on generative modeling of temporal graphs is at a nascent stage with significant scope for improvement. First, existing generative models do not scale with either the time horizon or the number of nodes. Second, existing techniques are transductive in nature and thus do not facilitate knowledge transfer. Finally, due to their reliance on oneto-one node mapping from source to the generated graph, existing models leak node identity information and do not allow up-scaling/down-scaling the source graph size. In this paper, we bridge these gaps with a novel generative model called TIGGER. TIGGER derives its power through a combination of temporal point processes with auto-regressive modeling enabling both transductive and inductive variants. Through extensive experiments on real datasets, we establish TIGGER generates graphs of superior fidelity, while also being up to 3 orders of magnitude faster than the state-of-the-art.

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AAAI Conference 2021 Conference Paper

Neural Latent Space Model for Dynamic Networks and Temporal Knowledge Graphs

Tony Gracious
Shubham Gupta
Arun Kanthali
Rui M. Castro
Ambedkar Dukkipati

Although static networks have been extensively studied in machine learning, data mining, and AI communities for many decades, the study of dynamic networks has recently taken center stage due to the prominence of social media and its effects on the dynamics of social networks. In this paper, we propose a statistical model for dynamically evolving networks, together with a variational inference approach. Our model, Neural Latent Space Model with Variational Inference, encodes edge dependencies across different time snapshots. It represents nodes via latent vectors and uses interaction matrices to model the presence of edges. These matrices can be used to incorporate multiple relations in heterogeneous networks by having a separate matrix for each of the relations. To capture the temporal dynamics, both node vectors and interaction matrices are allowed to evolve with time. Existing network analysis methods use representation learning techniques for modelling networks. These techniques are different for homogeneous and heterogeneous networks because heterogeneous networks can have multiple types of edges and nodes as opposed to a homogeneous network. Unlike these, we propose a unified model for homogeneous and heterogeneous networks in a variational inference framework. Moreover, the learned node latent vectors and interaction matrices may be interpretable and therefore provide insights on the mechanisms behind network evolution. We experimented with a single step and multi-step link forecasting on real-world networks of homogeneous, bipartite, and heterogeneous nature, and demonstrated that our model significantly outperforms existing models.

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EAAI Journal 2020 Journal Article

A memory guided sine cosine algorithm for global optimization

Shubham Gupta
Kusum Deep
Andries P. Engelbrecht

Real-world optimization problems demand an algorithm which properly explores the search space to find a good solution to the problem. The sine cosine algorithm (SCA) is a recently developed and efficient optimization algorithm, which performs searches using the trigonometric functions sine and cosine. These trigonometric functions help in exploring the search space to find an optimum. However, in some cases, SCA becomes trapped in a sub-optimal solution due to an inefficient balance between exploration and exploitation. Therefore, in the present work, a balanced and explorative search guidance is introduced in SCA for candidate solutions by proposing a novel algorithm called the memory guided sine cosine algorithm (MG-SCA). In MG-SCA, the number of guides is decreased with increase in the number of iterations to provide a sufficient balance between exploration and exploitation. The performance of the proposed MG-SCA is analysed on benchmark sets of classical test problems, IEEE CEC 2014 problems, and four well known engineering benchmark problems. The results on these applications demonstrate the competitive ability of the proposed algorithm as compared to other algorithms.

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AAAI Conference 2019 Conference Paper

A Generative Model for Dynamic Networks with Applications

Shubham Gupta
Gaurav Sharma
Ambedkar Dukkipati

Networks observed in real world like social networks, collaboration networks etc. , exhibit temporal dynamics, i. e. nodes and edges appear and/or disappear over time. In this paper, we propose a generative, latent space based, statistical model for such networks (called dynamic networks). We consider the case where the number of nodes is fixed, but the presence of edges can vary over time. Our model allows the number of communities in the network to be different at different time steps. We use a neural network based methodology to perform approximate inference in the proposed model and its simplified version. Experiments done on synthetic and real world networks for the task of community detection and link prediction demonstrate the utility and effectiveness of our model as compared to other similar existing approaches.

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ICML Conference 2017 Conference Paper

Attentive Recurrent Comparators

Pranav Shyam
Shubham Gupta
Ambedkar Dukkipati

Rapid learning requires flexible representations to quickly adopt to new evidence. We develop a novel class of models called Attentive Recurrent Comparators (ARCs) that form representations of objects by cycling through them and making observations. Using the representations extracted by ARCs, we develop a way of approximating a dynamic representation space and use it for one-shot learning. In the task of one-shot classification on the Omniglot dataset, we achieve the state of the art performance with an error rate of 1. 5\%. This represents the first super-human result achieved for this task with a generic model that uses only pixel information.

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