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Enhancing molecular property prediction by fusing graph and sequence encoder representations

This work introduces a novel fusion learning framework that integrates both molecular graph structure information and molecular sequence contextual information of molecules to enhance molecular representation learning.

Sonika Rajesh Pillai, Madhurya R Nair, Vyshakh G Nair, Anuraj Mohan

Improving anti-money laundering in bitcoin using evolving graph convolutions and deep neural decision forest

Anuraj Mohan, Karthika P V, Parvathi Sankar, Maya Manohar K

Representation Learning for Temporal Networks Using Temporal Random Walk and Deep Autoencoder

In this paper, we propose TNE-DNN, an efficient algorithm to construct time-aware embeddings from temporal networks. We also provide a general framework for associating temporal information into a deep learning based network embedding method.

Anuraj Mohan, K. V. Pramod

Transforming supply chain network and logistics using blockchain - a Survey

Sreerakhi V, Balagopal N, Anuraj Mohan

Temporal network embedding using graph attention network

In this work, we propose a temporal graph attention network (TempGAN), where the aim is to learn representations from continuous-time temporal network by preserving the temporal proximity between nodes of the network.

Anuraj Mohan, K. V. Pramod

Fine-grained entity type classification using GRU with self-attention

Dhrisya K, Remya G, Anuraj Mohan

Link prediction in dynamic networks using time-aware network embedding and time series forecasting

In this work, we propose a time-aware network embedding method which generates node embeddings by capturing the temporal dynamics of evolving networks.

Anuraj Mohan, K. V. Pramod

Network representation learning: models, methods and applications

In this research, we provide a detailed study of the latest advancements in the field of network representation learning (also called network embedding).

Anuraj Mohan, K. V. Pramod

Temporal Link Prediction: A Survey

Aswathy Divakaran, Anuraj Mohan

A Social Recommender System using Deep Architecture and Network Embedding

In this work, we propose a hybrid method by modeling a joint optimization function which extends deep Autoencoder with top-k semantic social information for social recommendation.

Nisha C C, Anuraj Mohan