118 citations · 134 across the 7 of their papers we have counts for
8 papers
A Deep Learning Framework for Evaluating Dynamic Network Generative Models and Anomaly Detection
Alireza Rashnu, Sadegh Aliakbary
Understanding dynamic systems like disease outbreaks, social influence, and information diffusion requires effective modeling of complex networks. Traditional evaluation methods fo…
Automatic Generation of Adaptive Network Models based on Similarity to the Desired Complex Network
Niousha Attar, Sadegh Aliakbary
Complex networks have become powerful mechanisms for studying a variety of realworld systems. Consequently, many human-designed network models are proposed that reproduce nontrivia…
Towards quantitative methods to assess network generative models
Vahid Mostofi, Sadegh Aliakbary
Assessing generative models is not an easy task. Generative models should synthesize graphs which are not replicates of real networks but show topological features similar to real…
Predicting citation counts based on deep neural network learning techniques
Ali Abrishami, Sadegh Aliakbary
With the growing number of published scientific papers world-wide, the need to evaluation and quality assessment methods for research papers is increasing. Scientific fields such a…
Feature Extraction from Degree Distribution for Comparison and Analysis of Complex Networks
Sadegh Aliakbary, Jafar Habibi, Ali Movaghar
The degree distribution is an important characteristic of complex networks. In many data analysis applications, the networks should be represented as fixed-length feature vectors a…
Learning an Integrated Distance Metric for Comparing Structure of Complex Networks
Sadegh Aliakbary, Sadegh Motallebi, Jafar Habibi +1
Graph comparison plays a major role in many network applications. We often need a similarity metric for comparing networks according to their structural properties. Various network…
Quantification and Comparison of Degree Distributions in Complex Networks
Sadegh Aliakbary, Jafar Habibi, Ali Movaghar
The degree distribution is an important characteristic of complex networks. In many applications, quantification of degree distribution in the form of a fixed-length feature vector…
Generative Model Selection Using a Scalable and Size-Independent Complex Network Classifier
Sadegh Motallebi, Sadegh Aliakbary, Jafar Habibi
Real networks exhibit nontrivial topological features such as heavy-tailed degree distribution, high clustering, and small-worldness. Researchers have developed several generative…