About Me

I am currently an AI Research Scientist at the Institute for Software Integrated Systems, Vanderbilt University, TN, USA. Prior to that, I was a postdoc at Vanderbilt University. I received my PhD in Computer Science from Information Technology University (ITU) and was a member of Artificial Intelligence Lab. My research primarily intersects the fields of data science and machine learning, with a focus on graph representation learning, social network analysis, and graph theory with applications in Anhedonia detection, drug discovery and development, social networks and education. I actively participates in top conferences and journals in these domains, contributing through both publications and serving as a PC member and a reviewer. My works have been published in renowned venues such as NeurIPS, Neurocomputing, Applied Soft Computing, and TKDE, among others.

Latest

Paper accepted
(Jan. 2024)
(Jan. 2024)
Attending NeurIPS 2023 (Dec. 2023)

I will be attending NeurIPS 2023 in New Orleans, LA and will be presenting our poster on benchmarks for graph machine learning in Brain Connectomics. If you are attending the conference, please feel free to stop by and chat, or drop me a message!

Paper accepted
Paper entitled MSDGSD: A Scalable Graph Descriptor for Processing Large Graphs accepted at IEEE Transaction on Computational Social Systems (Nov. 2023)
Paper accepted
Paper entitled Network Controllability Perspectives on Graph Representation accepted at IEEE Transaction on Knowledge and Data Engineering (TKDE) (Nov. 2023)
Joined Institute for Software Integrated Systems at Vanderbilt University as a Research Scientist! (Oct. 2023)
Graph Unlearning Review Preprint Released! (Sep. 2023)

Paper entitled Graph Unlearing: A Review is now online!

Invited to serve as a PC member for the 7th Workshop on Graph Techniques for Adversarial Activity Analytics, IEEE Big Data Conference 2023 (Aug. 2023)
Attending KDD 2023 (Aug. 2023)

I will be attending KDD 2023 in Long Beach, CA and will be presenting our paper on benchmarks for graph machine learning in Brain Connectomics. I am also co-organizaing Data Science for Social Good Workshop (DSSG) at KDD. If you are attending the conference, please feel free to stop by and chat, or drop me a message!

Release datasets and documentation! (June 2023)

We have released the documentation for NeuroGraph package!

Documentation       Website       GitHub       Package

Talk (March 2023)

I gave a guest lecture at Agriculture Sciences, Tennessee State University on "Machine Learning at the Forefront of Molecular Analysis". Link to the slides

Paper accepted (March 2023)

Paper entitled Sequential Graph Neural Networks for Code Vulnerability Identification accepted at HotSoS23

Paper accepted (Jan. 2023)

Paper entitled On Augmenting Topological Graph Representations for Attributed Graphs accepted at Applied Soft Computing (2023)

Paper accepted (Jan. 2023)

Paper entitled Circuit Design completion using Graph Neural Networks accepted at Neural Computing and Applications (2023)

Selected Publications

for complete list, please see my google scholar
NeuroGraph: Benchmarks for Graph Machine Learning in Brain Connectomics

Anwar Said, et al. "NeuroGraph: Benchmarks for Graph Machine Learning in Brain Connectomics." arXiv preprint arXiv:2306.06202 (2023).

Circuit design completion using graph neural networks

Said, A., Shabbir, M., Hassan, S. U., Hassan, Z. R., Ahmed, A., & Koutsoukos, X. (2023). On augmenting topological graph representations for attributed graphs. Applied Soft Computing, 110104.

On Augmenting Topological Graph Representations for Attributed Graphs

Anwar Said, Mudassir Shabbir, Brian Broll, Waseem Abbas, Peter Volgyesi, Xenofon Koutsoukos, Neural Computing and Applications 2023

DGSD: Distributed graph representation via graph statistical properties

Said, A., Hassan, S. U., Tuarob, S., Nawaz, R., & Shabbir, M. (2021) Future Generation Computer Systems, 119, 166-175

Detailed analysis of Ethereum network on transaction behavior, community structure and link prediction.

Said, Anwar, et al (2021) PeerJ Computer Science 7 : e815

NetKI: A kirchhoff index based statistical graph embedding in nearly linear time..

Said, A., Hassan, S. U., Abbas, W., & Shabbir, M. (2021), Neurocomputing, 433, 108-118.

Leveraging deep learning and SNA approaches for smart city policing in the developing world.

Hassan, S. U., Shabbir, M., Iqbal, S., Said, A., Kamiran, F., Nawaz, R., & Saif, U. (2019),International Journal of Information Management, 102045.

Mining network-level properties of Twitter altmetrics data.

Said, A., Bowman, T. D., Abbasi, R. A., Aljohani, N. R., Hassan, S. U., & Nawaz, R. (2019) Mining network-level properties of Twitter altmetrics data. Scientometrics, 120(1), 217-235.

Proactive caching at the edge leveraging influential user detection in cellular D2D networks.

Said, A., Shah, S. W. H., Farooq, H., Mian, A. N., Imran, A., & Crowcroft, J. (2018). Future Internet, 10(10), 93.

Exploiting social networks of Twitter in altmetrics big data

Imran, M., Akhtar, A., Said, A., Safder, I., Hassan, S. U., & Aljohani, N. R. (2018, September) In 23rd international conference on science and technology indicators (STI 2018) (pp. 12-14).

CC-GA: A clustering coefficient based genetic algorithm for detecting communities in social networks.

Said, A., Abbasi, R. A., Maqbool, O., Daud, A., & Aljohani, N. R. (2018). Applied Soft Computing, 63, 59-70.