Information on researchers - Saito Kazumi

(Last updated : 2022-12-09 16:58:36)

Saito Kazumi
Kanagawa University Faculty of Informatics Department of Computer Science

Kanagawa University Graduate School Graduate School of Science Course of Science (Field of Information Sciences)

Professor

■ Present specialized field

Intelligent informatics, Web informatics and service informatics

■ Book and Papers

1.	Papers	Efficient computation of target-oriented link criticalness centrality in uncertain graphs (Collaboration) 2021/10
2.	Papers	Multiple Perspective Centrality Measures Based on Facility Location Problem Under Inter-Group Competitive Environment (Collaboration) 2020/10
3.	Papers	Resampling-based Predictive Simulation Framework of Stochastic Diffusion Model for Identifying Top-K Influential Nodes (Collaboration) 2020/09
4.	Papers	Critical Node Identification based on Articulation Point Detection for Uncertain Network," International Journal of Networking and Computing (Collaboration) 2019/08
5.	Papers	Estimating Node Connectedness in Spatial Network under Stochastic Link Disconnection Based on Efficient Sampling (Collaboration) 2019/08
6.	Papers	Accelerating a Lloyd-Type k-Means Clustering Algorithm with Summable Lower Bounds in a Lower-Dimensional Space (Collaboration) 2018/11
7.	Papers	Accurate and Efficient Detection of Critical Links in Network to Minimize Information Loss (Collaboration) 2018/09
8.	Papers	Improving approximate extraction of functional similar regions from large-scale spatial networks based on greedy selection of representative nodes of different areas (Collaboration) 2018/07
9.	Papers	Pivot Generation Algorithm with a Complete Binary Tree for Efficient Exact Similarity Search (Collaboration) 2018/01
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■ Academic conference presentation

1.	2020	Efficient Computing of PageRank Scores on Exact Expected Transition Matrix of Large Uncertain Graph (Proc. of the 2020 IEEE International Conference on Big Data (BigData2020))
2.	2020	Maximizing Network Coverage Under the Presence of Time Constraint by Injecting Most Effective k-Links (Proc. of the 23th Internatioanal Conference on Discovery Science(DS2020))
3.	2020	Opening and Closing Dynamics of Competing Shop Groups over Spatial Networks (Proc. of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM2020))
4.	2020	Spatio-Temporal Clustering of Earthquakes Based on Average Magnitudes (Proc. of the 9th International Conference on Complex Networks and Their Applications(ComplexNetwork2020))
5.	2019	Construction of Histogram with Variable Bin-width based on Change Point Detection (Proc. of the 22th Internatioanal Conference on Discovery Science(DS2019))
6.	2019	Facility Location Problem on Network \\Based on Group Centrality Measure Considering Cooperation and Competition (Proc. of the 8th International Conference on Complex Networks and Their Applications(ComplexNetwork2019))
7.	2019	Resampling-based Framework for Unbiased Estimator of Node Centrality over Large Complex Network (Proc. of the 22th Internatioanal Conference on Discovery Science(DS2019))
8.	2018/12	A New Group Centrality Measure for Maximizing the Connectedness of Network under Uncertain Connectivity (the 7th International Conference on Complex Networks and Their Applications)
9.	2018/11	Critical Node Identification based on Articulation Point Detection for Network with Uncertain Connectivity (the 6th International Symposium on Computing and Networking)
10.	2018/10	Critical Link Identification based on Bridge Detection for Network with Uncertain Connectivity (the 24th International Symposium on Methodologies for Intelligent Systems)
11.	2018/08	Efficient Detection of Critical Links to Maintain Performance of Network with Uncertain Connectivity (the 15th Pacific Rim International Conference on Artificial Intelligence)
12.	2017/12	Fast Extraction Method of Functional Clusters from Large-Scale Spatial Networks Based on Transfer Learning (the 6th International Conference on Complex Networks and Their Applications)
13.	2017/11	Discovering Cooperative Structure among Online Items for Attention Dynamics (2017 IEEE International Conference on Data Mining Workshops)
14.	2017/10	Maximizing Network Performance based on Group Centrality by Creating Most Effective k-links (the 4th IEEE International Conference on Data Science and Advanced Analytics)
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■ Department laboratory expense researcher number

80379544