4 code implementations • 30 Jan 2023 • Siddharth Bhatia
We then extend the count-min sketch to a Higher-Order sketch to capture complex relations in graph data, and to reduce detecting suspicious dense subgraph problem to finding a dense submatrix in constant time.
no code implementations • NeurIPS 2021 • Yiwei Wang, Yujun Cai, Yuxuan Liang, Henghui Ding, Changhu Wang, Siddharth Bhatia, Bryan Hooi
To address this issue, our idea is to transform the temporal graphs using data augmentation (DA) with adaptive magnitudes, so as to effectively augment the input features and preserve the essential semantic information.
1 code implementation • NeurIPS 2021 • Koki Kawabata, Siddharth Bhatia, Rui Liu, Mohit Wadhwa, Bryan Hooi
In general, given a data stream of events with seasonal patterns that innovate over time, how can we effectively and efficiently forecast future events?
1 code implementation • 29 Jun 2021 • Siddharth Bhatia, Yiwei Wang, Bryan Hooi, Tanmoy Chakraborty
Specifically, the generative model learns to approximate the distribution of anomalous samples from the candidate set of graph snapshots, and the discriminative model detects whether the sampled snapshot is from the ground-truth or not.
1 code implementation • 8 Jun 2021 • Siddharth Bhatia, Mohit Wadhwa, Kenji Kawaguchi, Neil Shah, Philip S. Yu, Bryan Hooi
This higher-order sketch has the useful property of preserving the dense subgraph structure (dense subgraphs in the input turn into dense submatrices in the data structure).
1 code implementation • 7 Jun 2021 • Siddharth Bhatia, Arjit Jain, Shivin Srivastava, Kenji Kawaguchi, Bryan Hooi
Given a stream of entries over time in a multi-dimensional data setting where concept drift is present, how can we detect anomalous activities?
2 code implementations • 4 Apr 2021 • Rui Liu, Siddharth Bhatia, Bryan Hooi
Isconna does not actively explore or maintain pattern snippets; it instead measures the consecutive presence and absence of edge records.
1 code implementation • 23 Feb 2021 • Shivin Srivastava, Siddharth Bhatia, Lingxiao Huang, Lim Jun Heng, Kenji Kawaguchi, Vaibhav Rajan
In data containing heterogeneous subpopulations, classification performance benefits from incorporating the knowledge of cluster structure in the classifier.
1 code implementation • 3 Dec 2020 • Jiabao Zhang, Shenghua Liu, Wenting Hou, Siddharth Bhatia, HuaWei Shen, Wenjian Yu, Xueqi Cheng
Therefore, we propose a fast streaming algorithm, AugSplicing, which can detect the top dense blocks by incrementally splicing the previous detection with the incoming ones in new tuples, avoiding re-runs over all the history data at every tracking time step.
3 code implementations • 17 Sep 2020 • Siddharth Bhatia, Rui Liu, Bryan Hooi, Minji Yoon, Kijung Shin, Christos Faloutsos
Given a stream of graph edges from a dynamic graph, how can we assign anomaly scores to edges in an online manner, for the purpose of detecting unusual behavior, using constant time and memory?
1 code implementation • 17 Sep 2020 • Siddharth Bhatia, Arjit Jain, Pan Li, Ritesh Kumar, Bryan Hooi
Given a stream of entries in a multi-aspect data setting i. e., entries having multiple dimensions, how can we detect anomalous activities in an unsupervised manner?
Ranked #1 on Intrusion Detection on CIC-DDoS
1 code implementation • 17 Sep 2020 • Siddharth Bhatia, Arjit Jain, Bryan Hooi
Hence, in this work, we propose ExGAN, a GAN-based approach to generate realistic and extreme samples.
9 code implementations • 11 Nov 2019 • Siddharth Bhatia, Bryan Hooi, Minji Yoon, Kijung Shin, Christos Faloutsos
Given a stream of graph edges from a dynamic graph, how can we assign anomaly scores to edges in an online manner, for the purpose of detecting unusual behavior, using constant time and memory?
Ranked #1 on Anomaly Detection in Edge Streams on Darpa