Mostafa HaghirChehreghani - Amirkabir University of Technology

Supervised MSc Theses

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Thesis title

By

Date

1

Evaluating Users Credibility in Social Networks

Mohammad Moradi & Mostafa Haghirchehreghani

July 2023

2

embedding computation for heterogeneous data

Fatemeh Gholamzadehnasrabadi & Mostafa Haghirchehreghani

June 2023

3

Improving Abstractive Text Summarization

Mohammadjavad Zohrabi & Mostafa Haghirchehreghani

June 2022

4

Developing a scalable distributed framework for learning graph representationas

Moein Salimi & Mostafa Haghirchehreghani

October 2021

5

Evaluating the success of a business based on social networks market analysis

Seyed Mohammad Mirabdolbaghi & Mostafa Haghirchehreghani

December 2020

6

providing a method for link prediction in complex networks

Masoud Malek & Mostafa Haghirchehreghani

July 2020

7

Design and implementation of Iran stock market prediction sofware using machine learning techniques

Mehdi Salemi Mottaghi & Mostafa Haghirchehreghani

February 2020

Journal Papers

Portal Records

1

Batool Lakzaei, Mostafa Haghirchehreghani, Alireza Bagheri, "Disinformation detection using graph neural networks: a survey", ARTIFICIAL INTELLIGENCE REVIEW, February 2024 Vol. 57, Num. 3, Page 1-45, February 2024,

2

Mostafa Haghirchehreghani, "The embeddings world and Artificial General Intelligence", Cognitive Systems Research, December 2023 Vol. 84, Num. 3, Page 1-6, December 2023,

3

Mehdi Salemi Mottaghi, Mostafa Haghirchehreghani, "A deep comprehensive model for stock price prediction", Journal of Ambient Intelligence and Humanized Computing, June 2023 Vol. 0, Num. 0, Page 1-11, June 2023,

4

Mostafa Haghirchehreghani, Zahra Yaghoobi, "Improving empirical efficiency of CUR decomposition", JOURNAL OF SUPERCOMPUTING, January 2023 Vol. -, Num. 0, Page 1-17, January 2023,

5

Mostafa Haghirchehreghani, "On the theory of dynamic graph regression problem", COMPUTATIONAL & APPLIED MATHEMATICS, October 2022 Vol. 41, Num. 8, Page 1-17, October 2022,

6

Mostafa Haghirchehreghani, "On using affine sketches for multiple-response dynamic graph regression", JOURNAL OF SUPERCOMPUTING, October 2022 Vol. 0, Num. 0, Page 1-15, October 2022,

7

Mostafa Haghirchehreghani, "Half a decade of graph convolutional networks", Nature Machine Intelligence, March 2022 Vol. 4, Num. 3, Page 192-193, March 2022,

8

Mostafa Haghirchehreghani, "Sublinear update time randomized algorithms for dynamic graph regression", APPLIED MATHEMATICS AND COMPUTATION, December 2021 Vol. 410, Num. 0, Page 1-11, December 2021,

9

Mostafa Haghirchehreghani, Albert Bifet, Talel Abdessalem, "Exact and Approximate Algorithms for Computing Betweenness Centrality in Directed Graphs", FUNDAMENTA INFORMATICAE, November 2021 Vol. 182, Num. 3, Page 219-242, November 2021,

10

Mostafa Haghirchehreghani, "Dynamical algorithms for data mining and machine learning over dynamic graphs", Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery, November 2020 Vol. 0, Num. 0, Page 1-19, November 2020,

11

Morteza Haghir Chehreghani, Mostafa Haghirchehreghani, "Learning representations from dendrograms", MACHINE LEARNING, August 2020 Vol. 109, Num. 9, Page 1779-1802, August 2020,

12

Mostafa Haghirchehreghani, Talel Abdessalem, Albert Bifet, Meriem Bouzbila, "Sampling informative patterns from large single networks", FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, January 2020 Vol. 106, Num. 0, Page 653-658, January 2020,

Conference Papers

Portal Records

1

Zahra Moteshakerarani, Mostafa Haghirchehreghani, Morteza Haghir Chehreghani, "Non-uniform Sampling Methods for Large Itemset Mining ", IEEE International Conference on Big Data, IEEE BigData 2023, December 2023

2

Mahdi Ghanbari, Mostafa Haghirchehreghani, Morteza Haghir Chehreghani, "Graph Clustering Using Node Embeddings: An Empirical Study ", IEEE International Conference on Big Data (IEEE BigData 2022), December 2022

3

Sara Asghari, Mostafa Haghirchehreghani, Morteza Haghir Chehreghani, "On Using Node Indices and Their Correlations for Fake Account Detection ", IEEE International Conference on Big Data (IEEE BigData 2022), December 2022

4

Masoud Malek, Mostafa Haghirchehreghani, Ehsan Nazerfard, Morteza Haghir Chehreghani, "Shallow Node Representation Learning using Centrality Indices ", 2021 IEEE International Conference on Big Data (IEEE BigData 2021), December 2021

5

Mostafa Haghirchehreghani, "Subsampled Randomized Hadamard Transform for Regression of Dynamic Graphs ", 29TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT - CIKM 2020, October 2020

6

Mostafa Haghirchehreghani, Albert Bifet, Talel Abdessalem, "Adaptive Algorithms for Estimating Betweenness and k-path Centralities ", 28th ACM International Conference on Information and Knowledge Management, CIKM 2019, November 2019

7

Mostafa Haghirchehreghani, Masoud Rahgozar, Caro Lucas, Morteza Haghir Chehreghani, "Mining Maximal Embedded Unordered Tree Patterns ", 2007 IEEE Symposium on Computational Intelligence and Data Mining, March 2007

Taught Courses

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Course title

Description

Headlines

Date

1

(Big Data Analytics)

Analyzing the data that are generated with high velocity, variety and volume, is not possible with traditional techniques. The goal of this course is to familiarize students with advanced theories and algorithms that have been recently proposed to fa

Spring 2024

2

Applied Linear Algebra

In this course, those topics of linear algebra that are widely used in different areas of computer engineering, are presented and discussed. In particular, we familiarize students with linear equations, matrix algebra, deteminants, vector spaces, eig

Spring 2024

3

Discrete Mathematics

In this course, topics of Discrete Mathematics that are widely used in different areas of computer engineering, are presented and discussed. In particular, this course aims to improve reasoning and problem solving abilities of the students and famili

Fall 2023

4

(Complex Networks Analysis)

The objective of this course is to familiarize students with techniques of analyzing and processing complex networks. In this course, we theoretically and empirically investigate different types of complex networks. Furthermore, we study topics such