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Mohamed Bouguessa
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2020 – today
- 2024
- [j18]Nairouz Mrabah
, Mohamed Bouguessa, Riadh Ksantini:
A contrastive variational graph auto-encoder for node clustering. Pattern Recognit. 149: 110209 (2024) - [j17]Kamel Abdous
Hierarchical Aggregations for High-Dimensional Multiplex Graph Embedding. IEEE Trans. Knowl. Data Eng. 36(4): 1624-1637 (2024) - [c38]Kamel Abdous, Nairouz Mrabah, Mohamed Bouguessa:
A Geometric Perspective for High-Dimensional Multiplex Graphs. CIKM 2024: 4-13 - [c37]Armand Bandiang Massoua, Abdoulaye Baniré Diallo, Mohamed Bouguessa:
Towards Robust Time-to-Event Prediction: Integrating the Variational Information Bottleneck with Neural Survival Model. IJCNN 2024: 1-8 - [c36]Mohamed Amine Ouali, Mohamed Bouguessa, Riadh Ksantini:
DfHM: A Hierarchical Approach for Matching Pairs of Images Using Graph Attention Neural Networks. IJCNN 2024: 1-10 - 2023
- [j16]Etienne Gael Tajeuna
Modeling Regime Shifts in Multiple Time Series. ACM Trans. Knowl. Discov. Data 17(8): 115:1-115:31 (2023) - [j15]Nairouz Mrabah
Rethinking Graph Auto-Encoder Models for Attributed Graph Clustering. IEEE Trans. Knowl. Data Eng. 35(9): 9037-9053 (2023) - [c35]Nairouz Mrabah, Mohamed Mahmoud Amar, Mohamed Bouguessa, Abdoulaye Baniré Diallo:
Exploring the Interaction between Local and Global Latent Configurations for Clustering Single-Cell RNA-Seq: A Unified Perspective. AAAI 2023: 9235-9242 - [c34]Ahmed F. M. Fahmy
Tracking User Sentiment Changes on Social Networks. ASONAM 2023: 454-458 - [c33]Etienne Gael Tajeuna, Ahmed F. M. Fahmy, Mohamed Bouguessa:
Modeling Time-Varying User Attitudes in Social Media. COMPSAC 2023: 976-977 - [c32]Nairouz Mrabah, Mohamed Bouguessa, Riadh Ksantini:
Adversarial Deep Embedded Clustering: On a better trade-off between Feature Randomness and Feature Drift (Extended abstract). ICDE 2023: 3887-3888 - [c31]Nairouz Mrabah, Mohamed Bouguessa, Mohamed Fawzi Touati, Riadh Ksantini:
Rethinking Graph Auto-Encoder Models for Attributed Graph Clustering (Extended abstract). ICDE 2023: 3891-3892 - [c30]Nairouz Mrabah, Mohamed Mahmoud Amar, Mohamed Bouguessa, Abdoulaye Baniré Diallo:
Toward Convex Manifolds: A Geometric Perspective for Deep Graph Clustering of Single-cell RNA-seq Data. IJCAI 2023: 4855-4863 - [c29]Nairouz Mrabah, Mohamed Bouguessa, Riadh Ksantini:
Beyond The Evidence Lower Bound: Dual Variational Graph Auto-Encoders For Node Clustering. SDM 2023: 100-108 - [i11]Nairouz Mrabah, Mohamed Bouguessa, Riadh Ksantini:
A Contrastive Variational Graph Auto-Encoder for Node Clustering. CoRR abs/2312.16830 (2023) - [i10]Kamel Abdous, Nairouz Mrabah, Mohamed Bouguessa:
Hierarchical Aggregations for High-Dimensional Multiplex Graph Embedding. CoRR abs/2312.16834 (2023) - 2022
- [j14]Nairouz Mrabah
Adversarial Deep Embedded Clustering: On a Better Trade-off Between Feature Randomness and Feature Drift. IEEE Trans. Knowl. Data Eng. 34(4): 1603-1617 (2022) - [c28]Etienne Gael Tajeuna, Mohamed Bouguessa:
A Time-Dependent-Based Approach to Enhance Self-Harm Prediction. ASONAM 2022: 492-495 - [c27]Etienne Gael Tajeuna, Mohamed Bouguessa, Shengrui Wang:
A Longitudinal Study of Customer Electricity Load Profiles. COMPSAC 2022: 289-294 - [c26]Mohamed Amine Ouali, Mohamed Bouguessa, Riadh Ksantini:
Graph Attention Network for Camera Relocalization on Dynamic Scenes. DSAA 2022: 1-10 - [c25]Nairouz Mrabah
Escaping Feature Twist: A Variational Graph Auto-Encoder for Node Clustering. IJCAI 2022: 3351-3357 - [c24]Etienne Gael Tajeuna, Mohamed Bouguessa:
Dynamic Cox-Regression for Motif Prediction in Co-Evolving Time Series Data. IJCNN 2022: 1-10 - [i9]Mohamed Amine Ouali, Mohamed Bouguessa, Riadh Ksantini:
Graph Attention Network for Camera Relocalization on Dynamic Scenes. CoRR abs/2209.15056 (2022) - 2021
- [j13]Maroun Haddad, Mohamed Bouguessa:
Exploring the representational power of graph autoencoder. Neurocomputing 457: 225-241 (2021) - [j12]Maroun Haddad
TopoDetect: Framework for topological features detection in graph embeddings. Softw. Impacts 10: 100139 (2021) - [j11]Mohamed Bouguessa
BiNeTClus: Bipartite Network Community Detection Based on Transactional Clustering. ACM Trans. Intell. Syst. Technol. 12(1): 6:1-6:26 (2021) - [j10]Etienne Gael Tajeuna, Mohamed Bouguessa
Mining Customers' Changeable Electricity Consumption for Effective Load Forecasting. ACM Trans. Intell. Syst. Technol. 12(4): 47:1-47:26 (2021) - [i8]Fares Ben Slimane, Mohamed Bouguessa:
Context Matters: Self-Attention for Sign Language Recognition. CoRR abs/2101.04632 (2021) - [i7]Maroun Haddad, Mohamed Bouguessa:
Exploring the Representational Power of Graph Autoencoder. CoRR abs/2106.12005 (2021) - [i6]Nairouz Mrabah, Mohamed Bouguessa, Mohamed Fawzi Touati, Riadh Ksantini:
Rethinking Graph Auto-Encoder Models for Attributed Graph Clustering. CoRR abs/2107.08562 (2021) - [i5]Etienne Gael Tajeuna, Mohamed Bouguessa, Shengrui Wang:
Modeling Regime Shifts in Multiple Time Series. CoRR abs/2109.09692 (2021) - [i4]Maroun Haddad, Mohamed Bouguessa:
TopoDetect: Framework for Topological Features Detection in Graph Embeddings. CoRR abs/2110.04173 (2021) - 2020
- [c23]Fares Ben Slimane, Mohamed Bouguessa:
Context Matters: Self-Attention for Sign Language Recognition. ICPR 2020: 7884-7891
2010 – 2019
- 2019
- [j9]Etienne Gael Tajeuna
Modeling and Predicting Community Structure Changes in Time-Evolving Social Networks. IEEE Trans. Knowl. Data Eng. 31(6): 1166-1180 (2019) - [c22]Jehan Rubin, Adel Nassim Henniche, Naouel Moha, Mohamed Bouguessa, Nabila Bousbia:
Sniffing Android code smells: an association rules mining-based approach. MOBILESoft@ICSE 2019: 123-127 - [i3]Ziwei He, Etienne Gael Tajeuna, Shengrui Wang, Mohamed Bouguessa:
A Comparative Study of Different Approaches for Tracking Communities in Evolving Social Networks. CoRR abs/1903.07784 (2019) - [i2]Nairouz Mrabah, Mohamed Bouguessa, Riadh Ksantini:
Adversarial Deep Embedded Clustering: on a better trade-off between Feature Randomness and Feature Drift. CoRR abs/1909.11832 (2019) - 2018
- [c21]Mohamed Bouguessa, Amani Chouchane:
A Statistical Framework for Handling Network Anomalies. ASONAM 2018: 709-714 - [c20]Etienne Gael Tajeuna, Mohamed Bouguessa, Shengrui Wang:
A Network-Based Approach to Enhance Electricity Load Forecasting. ICDM Workshops 2018: 266-275 - 2017
- [j8]Oualid Boutemine, Mohamed Bouguessa:
Mining Community Structures in Multidimensional Networks. ACM Trans. Knowl. Discov. Data 11(4): 51:1-51:36 (2017) - [c19]Oualid Boutemine, Mohamed Bouguessa:
MCDA: A Parameterless Algorithm for Detecting Communities in Multidimensional Networks. ASONAM 2017: 291-296 - [c18]Ziwei He, Etienne Gael Tajeuna, Shengrui Wang, Mohamed Bouguessa:
A Comparative Study of Different Approaches for Tracking Communities in Evolving Social Networks. DSAA 2017: 89-98 - [c17]Amani Chouchane, Mohamed Bouguessa:
Identifying Anomalous Nodes in Multidimensional Networks. DSAA 2017: 601-610 - [c16]Louis Chartrand, Jackie Chi Kit Cheung, Mohamed Bouguessa:
Detecting Large Concept Extensions for Conceptual Analysis. MLDM 2017: 78-90 - [c15]Etienne Gael Tajeuna, Mohamed Bouguessa, Shengrui Wang:
Survival analysis for modeling critical events that communities may undergo in dynamic social networks. SAC 2017: 1068-1075 - [i1]Louis Chartrand, Jackie Chi Kit Cheung, Mohamed Bouguessa:
Detecting Large Concept Extensions for Conceptual Analysis. CoRR abs/1706.05723 (2017) - 2016
- [c14]Babak Khosravifar, Mohamed Bouguessa:
Using Support Vector Machines for Intelligent Service Agents Decision Making. MLDM 2016: 73-87 - [c13]Etienne Gael Tajeuna, Mohamed Bouguessa, Shengrui Wang:
Tracking Communities over Time in Dynamic Social Network. MLDM 2016: 341-345 - 2015
- [j7]Mohamed Bouguessa:
Clustering categorical data in projected spaces. Data Min. Knowl. Discov. 29(1): 3-38 (2015) - [j6]Mohamed Bouguessa:
A practical outlier detection approach for mixed-attribute data. Expert Syst. Appl. 42(22): 8637-8649 (2015) - [j5]Mohamed Bouguessa, Lotfi Ben Romdhane:
Identifying Authorities in Online Communities. ACM Trans. Intell. Syst. Technol. 6(3): 30:1-30:23 (2015) - [c12]Farnoosh Fathaliani, Mohamed Bouguessa:
A model-based approach for identifying spammers in social networks. DSAA 2015: 1-9 - [c11]Etienne Gael Tajeuna, Mohamed Bouguessa, Shengrui Wang:
Tracking the evolution of community structures in time-evolving social networks. DSAA 2015: 1-10 - 2014
- [j4]Mohamed Bouguessa:
A Mixture Model-Based Combination Approach for Outlier Detection. Int. J. Artif. Intell. Tools 23(4) (2014) - [c10]Mohamed Bouguessa, Rokia Missaoui, Mohamed Talbi:
A Novel Approach for Detecting Community Structure in Networks. ICTAI 2014: 469-477 - 2013
- [c9]Emilie Au, Mohamed Bouguessa, Shengrui Wang:
Document Modeling Using Syntactic and Semantic Information. AINA Workshops 2013: 203-206 - 2012
- [c8]Mohamed Bouguessa:
Modeling Outlier Score Distributions. ADMA 2012: 713-725 - [c7]Mohamed Bouguessa:
Unsupervised Anomaly Detection in Transactional Data. ICMLA (1) 2012: 526-531 - [c6]Mohamed Bouguessa:
A Probabilistic Combination Approach to Improve Outlier Detection. ICTAI 2012: 666-673 - 2011
- [c5]Mohamed Bouguessa:
An Unsupervised Approach for Identifying Spammers in Social Networks. ICTAI 2011: 832-840 - [c4]Mohamed Bouguessa:
A Practical Approach for Clustering Transaction Data. MLDM 2011: 265-279 - 2010
- [j3]Mohamed Bouguessa, Shengrui Wang, Benoît Dumoulin:
Discovering Knowledge-Sharing Communities in Question-Answering Forums. ACM Trans. Knowl. Discov. Data 5(1): 3:1-3:49 (2010)
2000 – 2009
- 2009
- [j2]Mohamed Bouguessa, Shengrui Wang:
Mining Projected Clusters in High-Dimensional Spaces. IEEE Trans. Knowl. Data Eng. 21(4): 507-522 (2009) - 2008
- [c3]Mohamed Bouguessa, Benoît Dumoulin, Shengrui Wang:
Identifying authoritative actors in question-answering forums: the case of Yahoo! answers. KDD 2008: 866-874 - 2007
- [c2]Mohamed Bouguessa, Shengrui Wang:
PCGEN: A Practical Approach to Projected Clustering and its Application to Gene Expression Data. CIDM 2007: 661-667 - 2006
- [j1]Mohamed Bouguessa, Shengrui Wang, Haojun Sun:
An objective approach to cluster validation. Pattern Recognit. Lett. 27(13): 1419-1430 (2006) - [c1]Mohamed Bouguessa, Shengrui Wang, Qingshan Jiang:
A K-means-based Algorithm for Projective Clustering. ICPR (1) 2006: 888-891
Coauthor Index
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last updated on 2024-11-15 20:39 CET by the dblp team
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