Md. Saidur Rahman

Md Nurul Muttakin, Md Iqbal Hossain, Md Saidur Rahman

Overlapping community detection is a key problem in graph mining. Some research has considered applying graph convolutional networks (GCN) to tackle the problem. However, it is still challenging to incorporate deep graph convolutional networks in the case of general irregular graphs. In this study, we design a deep dynamic residual graph convolutional network (DynaResGCN) based on our novel dynamic dilated aggregation mechanisms and a unified end-to-end encoder-decoder-based framework to detect overlapping communities in networks. The deep DynaResGCN model is used as the encoder, whereas we incorporate the Bernoulli-Poisson (BP) model as the decoder. Consequently, we apply our overlapping community detection framework in a research topics dataset without having ground truth, a set of networks from Facebook having a reliable (hand-labeled) ground truth, and in a set of very large co-authorship networks having empirical (not hand-labeled) ground truth. Our experimentation on these datasets shows significantly superior performance over many state-of-the-art methods for the detection of overlapping communities in networks.

Md Saidur Rahman, Foutse Khomh, Alaleh Hamidi et al.

Nowadays, intelligent systems and services are getting increasingly popular as they provide data-driven solutions to diverse real-world problems, thanks to recent breakthroughs in Artificial Intelligence (AI) and Machine Learning (ML). However, machine learning meets software engineering not only with promising potentials but also with some inherent challenges. Despite some recent research efforts, we still do not have a clear understanding of the challenges of developing ML-based applications and the current industry practices. Moreover, it is unclear where software engineering researchers should focus their efforts to better support ML application developers. In this paper, we report about a survey that aimed to understand the challenges and best practices of ML application development. We synthesize the results obtained from 80 practitioners (with diverse skills, experience, and application domains) into 17 findings; outlining challenges and best practices for ML application development. Practitioners involved in the development of ML-based software systems can leverage the summarized best practices to improve the quality of their system. We hope that the reported challenges will inform the research community about topics that need to be investigated to improve the engineering process and the quality of ML-based applications.