HEMZA Loucif
حمزة لوصيف
hemza.loucif@univ-msila.dz
0697874607
- Informatics Department
- Faculty of Mathematics and Informatics
- Grade MCA
About Me
Location
BBA, BBA
BBA, ALGERIA
Code RFIDE- 1988-05-13 00:00:00
-
HEMZA Loucif birthday
- 2025-11-25
-
2025-11-25
Dynamic Spammer Detection using deep learning with temporal graph embeddings
Spammers in online social networks continuously adapt their strategies, making detection a challenging and dynamic task. While traditional machine learning models and static deep learning approaches such as CNNs achieve good performance, they often fail to capture the temporal evolution of user behavior and network interactions. In this paper, we propose a novel deep learning framework for dynamic spammer detection that combines Principal Component Analysis (PCA) for feature reduction, Convolutional Neural Networks (CNNs) for local content feature extraction, and Temporal Graph Embeddings (TGEs) to capture evolving interaction patterns over time. Unlike prior static models, our approach explicitly models the dynamics of user behavior and relational changes in the social graph. Experiments conducted on benchmark Twitter datasets demonstrate that our hybrid PCA–CNN–TGE model significantly outperforms classical baselines (ANN, CNN, SVM) and static hybrid models, achieving an F1-score of 94 %. The results highlight the importance of temporal graph learning for robust and adaptive spammer detection in social networks. Keywords: Spam, Cybersecurity, CNN, Social Networks, Temporal Graph Embeddings, PCA.
Citation
Hemza Loucif , ,(2025-11-25), Dynamic Spammer Detection using deep learning with temporal graph embeddings,The 2nd International Workshop on Machine Learning and Deep Learning (WMLDL25) November 25, 2025 – M’Sila, Algeria,M’Sila, Algeria
- 2025-11-25
-
2025-11-25
Dynamic spammer detection using deep learning with temporal graph embeddings
Spammers in online social networks continuously adapt their strategies, making detection a challenging and dynamic task. While traditional machine learning models and static deep learning approaches such as CNNs achieve good performance, they often fail to capture the temporal evolution of user behavior and network interactions. In this paper, we propose a novel deep learning framework for dynamic spammer detection that combines Principal Component Analysis (PCA) for feature reduction, Convolutional Neural Networks (CNNs) for local content feature extraction, and Temporal Graph Embeddings (TGEs) to capture evolving interaction patterns over time. Unlike prior static models, our approach explicitly models the dynamics of user behavior and relational changes in the social graph. Experiments conducted on benchmark Twitter datasets demonstrate that our hybrid PCA CNN–TGE model significantly outperforms classical baselines (ANN, CNN, SVM) and static hybrid models, achieving an F1-score of 94 %. The results highlight the importance of temporal graph learning for robust and adaptive spammer detection in social networks.
Citation
Hemza Loucif , Samir Akhrouf , ,(2025-11-25), Dynamic spammer detection using deep learning with temporal graph embeddings,Second International Workshop on Machine Learning and Deep Learning,Mohamed Boudif University M'Sila, Faculté MI
Default case...
- 2021
-
2021
A Recursive Model to Measure Influence in Subscription Networks: A Case Study using Twitter.
This paper presents a new version of one of the models that we have proposed to measure the influence of web users in social networks like Facebook. The new version enhanced the previous one through the incorporation of two substantial modules, namely the global impression that the postings of the potential influencer get from his followers and the entropy which manifests the quantity of information carried by those postings. The comparison of our model with the precedent version and the PageRank benchmark has shown the effectiveness of our updates and the importance of incorporating the entropy and the global impression factors in its formulation.
Citation
Hemza Loucif , Samir Akhrouf , ,(2021), A Recursive Model to Measure Influence in Subscription Networks: A Case Study using Twitter.,International Conference on “Managing Business through Web Analytics" ICMBWA2020,,Université Djilali Bounaama – Khemis Miliana,