Graduate level · Concordia University, Algorithms & Complexity Lab, Department of Computer Science and Software Engineering · Montreal, Québec, Canada ·
Researching Algorithms Design & Analysis, Graph Theory, and Social Network Analysis
Working in the Algorithms & Complexity Lab
Under the supervision of Professor Hovhannes Harutyunyan
Date: Aug 2024 – Now
My key role consisted of:
Designed Spider, a graph community detection algorithm combining geodesic expansion, modularity-guided refinement, and greedy merge matching.
Benchmarked Spider on 14 real-world networks (up to 8,035 nodes / 183,663 edges) against Leiden, Louvain, and Infomap, achieving 8–15% improvements in NMI, modularity, and F1-score.
Applied metric backbone sparsification, achieving an average 65% edge reduction, and introduced Weighted Average Geodesic Distance Modularity (wGDM) to normalize and balance GDM for local community quality evaluation.
Built a fully reproducible experimental pipeline with fixed random seeds, baseline implementations, and automated evaluation scripts.
Undergraduate level · Vali-e-Asr University of Rafsanjan, Department of Computer Engineering · Rafsanjān, Kerman, Iran ·
Field of Research: Community Detection (Graph Algorithms)
Supervisor: Dr. Fahimeh Dabaghi-Zarandi
Department: “Computer Engineering” department of “Vali-e-Asr University of Rafsanjan”.
Date: Aug 2021 – March 2024
My key role consisted of:
Conducted a comprehensive review of prior work in graph-based community detection.
Designed and implemented CRLG, a randomized community detection framework leveraging both local and global network information.
Developed weighted probabilistic seeding and similarity-driven community assignment with heuristic community merging.
Implemented and evaluated the framework in MATLAB and Python, including validation, testing, and performance tuning.
Evaluated on real-world networks and GN/LFR benchmarks, achieving up to 10% improvement over LCDR, MOACO, Node2Vec-SC, NE-N2V, CDASS, and TS using NMI, modularity, and density metrics.