u540

LLMSurvey

The official GitHub page for the survey paper "A Survey of Large Language Models".

pygod

A Python Library for Graph Outlier Detection (Anomaly Detection)

dynamic-graph-papers

Archive of Temporal Knowledge Reasoning in Social Network and Knowledge Graph

office-exploit-case-study

GCN_AnomalyDetection

Code for Deep Anomaly Detection on Attributed Networks (SDM2019)

awesome-TS-anomaly-detection_zh

GADBench

"GADBench: Revisiting and Benchmarking Supervised Graph Anomaly Detection" in NeurIPS 2023

AnoGraph

Sketch-Based Anomaly Detection in Streaming Graphs

ANEMONE

[CIKM 2021] A PyTorch implementation of "ANEMONE: Graph Anomaly Detection with Multi-Scale Contrastive Learning".

GRADATE

An official source code for paper "Graph Anomaly Detection via Multi-Scale Contrastive Learning Networks with Augmented View", accepted by AAAI 2023.

SL-GAD

[TKDE 2021] A PyTorch implementation of "Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detection".

TADDY_pytorch

A Pytorch implement of paper "Anomaly detection in dynamic graphs via transformer" (TADDY).

Graph-Anomaly-Loss

TNNLS: A Synergistic Approach for Graph Anomaly Detection with Pattern Mining and Feature Learning; CIKM'20: Error-bounded Graph Anomaly Loss for GNNs.

ACT

The official PyTorch implementation of Cross-Domain Graph Anomaly Detection via Anomaly-aware Contrastive Alignment (AAAI2023, to appear).

sbustreamspot-core

Core streaming heterogeneous graph clustering and anomaly detection code (KDD 2016)

KGist

Knowledge Graph summarization for anomaly/error detection & completion (WebConf '20)

GHRN

[WWW 2023] "Addressing Heterophily in Graph Anomaly Detection: A Perspective of Graph Spectrum" by Yuan Gao, Xiang Wang, Xiangnan He, Zhenguang Liu, Huamin Feng, Yongdong Zhang

ComGA

The paper " ComGA:Community-Aware Attributed Graph Anomaly Detection" was accepted by WSDM 2022.

KDD19-AnomRank

Anomaly Detection in Dynamic Graphs

GLocalKD

Implementation of the paper Deep Graph-level Anomaly Detection by Glocal Knowledge Distillation(WSDM22)

Multi-Scale-One-Class-Recurrent-Neural-Networks

Code for KDD 2021 paper "Multi-Scale One-Class Recurrent Neural Networks \\for Discrete Event Sequence Anomaly Detection"

DeepLog

Anomaly detection is a critical step towards building a secure and trustworthy system. The primary purpose of a system log is to record system states and significant events at various critical points to help debug system failures and perform root cause analysis. Such log data is universally available in nearly all computer systems. Log data is an important and valuable resource for understanding system status and performance issues; therefore, the various system logs are naturally excellent source of information for online monitoring and anomaly detection. We propose DeepLog, a deep neural network model utilizing Long Short-Term Memory (LSTM), to model a system log as a natural language sequence. This allows DeepLog to automatically learn log patterns from normal execution, and detect anomalies when log patterns deviate from the model trained from log data under normal execution.

ABNORMAL-HUMAN-ACTIVITY-DETECTION-SYSTEM

# Abnormal-Human-Activity-Detection With the increase in the amount of anti-social activities taking place in the environment, security has been given the utmost importance lately. Therefore, organizations require a constant monitoring of people and their interactions. Since this constant monitoring of data by humans to judge if the events are abnormal is a near impossible task as it requires a lot of workforce and constant attention. Therefore, the challenge that comes up is the demand for an automatic and intelligent analysis for such video sequences. Our project comes forward as an attempt to provide solution to such a problem as the model developed is a smart surveillance system which can detect unusual or abnormal activity automatically. A method for representing the motion characteristics is described for detection and localization of unusual activities in the crowd scenes on a generalized framework which includes both a local and global range for detection of such activities.

neoHGT

🧬 Improved Genome-wide detection of horizontal gene transfer (HGT) events based on sequence homology search hit distribution statistics

BERT_SequenceTagging

A project finetuing BERT-lieked models for sequence tagging tasks (like Named Entity Recognition, Event Detection). Implemented by huggingface/transformers

build-event-sequence-dataset

Scripts for building datasets in varying formats for sequence detection task

SBADSG

Sketch-Based Anomaly Detection in Streaming Graphs

Abnormal-Human-Activity-Detection-System

With the increase in the amount of anti-social activities taking place in the environment, security has been given the utmost importance lately. Therefore, organizations require a constant monitoring of people and their interactions. Since this constant monitoring of data by humans to judge if the events are abnormal is a near impossible task as it requires a lot of workforce and constant attention. Therefore, the challenge that comes up is the demand for an automatic and intelligent analysis for such video sequences. Our project comes forward as an attempt to provide solution to such a problem as the model developed is a smart surveillance system which can detect unusual or abnormal activity automatically. A method for representing the motion characteristics is described for detection and localization of unusual activities in the crowd scenes on a generalized framework which includes both a local and global range for detection of such activities.

Abnormal-Human-Activity-Detection

With the increase in the amount of anti-social activities taking place in the environment, security has been given the utmost importance lately. Therefore, organizations require a constant monitoring of people and their interactions. Since this constant monitoring of data by humans to judge if the events are abnormal is a near impossible task as it requires a lot of workforce and constant attention. Therefore, the challenge that comes up is the demand for an automatic and intelligent analysis for such video sequences. Our project comes forward as an attempt to provide solution to such a problem as the model developed is a smart surveillance system which can detect unusual or abnormal activity automatically. A method for representing the motion characteristics is described for detection and localization of unusual activities in the crowd scenes on a generalized framework which includes both a local and global range for detection of such activities.

python-sequences

Python Sequences: A tool for detecting patterns and overlaps in data streams, from character strings to event sequences, without caching. Ideal for game input detection and sequence testing.