I have been attending deeplearning.ai’s specialization courses for a while, and I’ve completed the second part of the series this week. In this course, Prof Andrew Ng shares his experiences and best practices to build efficient machine learning pipelines. Although there is not any programming assignment in the course syllabus, there are 2 case studies that are very close to real-world experiments, and it is expected to give the right decisions on these scenarios. I strongly recommend practicing this short training.
This is an emerging research area targeting to solve social science theories by using computation, and most notably with Big Data and Machine learning techniques.
In these days, my research motivation is to find some insights by analyzing Twitter data to understand how English people react to Brexit referendum. There are various researches already made about this topic, and most of them are done by universities in England such as Imperial College London and the University of Bristol. I found it as a quite interesting research topic since social media is an important environment to present our ideas to the community and there is a need for more research to understand people’s opinions. I will give more detailed information about my study in the upcoming weeks. If you have any recommendation for me, please feel free to send me an email.
Good news, I joined to Data Science Research Group of Politecnico Milano as Research Fellow. At the moment, this is a position with a temporary contract since we need to see if I could contribute well to the research of this community. My advisor will be Marco Brambilla, who is known for his studies in Web Science and Software Engineering.
I am very pleased with becoming a member(even as a temporary researcher) of this valuable group. I believe I will gain a broad knowledge by making research with my professors and I will be able to solve a significant research problem in my Ph.D. thesis.
You can get more information about the research group from here. http://datascience.deib.polimi.it
Overlapping community detection allows placing one node to multiple communities. Up to now, many algorithms are proposed for this issue. However, their accuracy depends on the overlapping level of the structure. In this work, we aim at finding relatively small overlapping communities independently than their overlapping level. We define k-connected node groups as cohesive groups in which each pair of nodes has at least k different node-disjoint paths from one to another. We propose the algorithm EMOC first finding k-connected groups from the perspective of each node and second merging them to detect overlapping communities. We evaluate the accuracy of EMOC on artificial networks by comparing its results with foremost algorithms. The results indicate that EMOC can find small overlapping communities at any overlapping level. Results on the real-world network show that EMOC finds relatively small but consistent communities.