Ricky Laishram

According to our database1, Ricky Laishram authored at least 12 papers between 2016 and 2023.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
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Links

On csauthors.net:

Bibliography

2023
Skeletal Cores and Graph Resilience.
Proceedings of the Machine Learning and Knowledge Discovery in Databases: Research Track, 2023

2022
MCS+: An Efficient Algorithm for Crawling the Community Structure in Multiplex Networks.
ACM Trans. Knowl. Discov. Data, 2022

On Finding and Analyzing the Backbone of the k-Core Structure of a Graph.
Proceedings of the IEEE International Conference on Data Mining, 2022

2021
NetProtect: Network Perturbations to Protect Nodes against Entry-Point Attack.
Proceedings of the WebSci '21: 13th ACM Web Science Conference 2021, 2021

2020
Residual Core Maximization: An Efficient Algorithm for Maximizing the Size of the <i>k</i>-Core.
Proceedings of the 2020 SIAM International Conference on Data Mining, 2020

2019
Crawling the Community Structure of Multiplex Networks.
Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018
Measuring and Improving the Core Resilience of Networks.
Proceedings of the 2018 World Wide Web Conference on World Wide Web, 2018

Guidelines for Online Network Crawling: A Study of Data Collection Approaches and Network Properties.
Proceedings of the 10th ACM Conference on Web Science, 2018

2017
Predicted max degree sampling: Sampling in directed networks to maximize node coverage through crawling.
Proceedings of the 2017 IEEE Conference on Computer Communications Workshops, 2017

2016
Curie: A method for protecting SVM Classifier from Poisoning Attack.
CoRR, 2016

Link Prediction in Social Networks with Edge Aging.
Proceedings of the 28th IEEE International Conference on Tools with Artificial Intelligence, 2016

Max-node sampling: An expansion-densification algorithm for data collection.
Proceedings of the 2016 IEEE International Conference on Big Data (IEEE BigData 2016), 2016


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