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AsiaInfo Technology: New Tech Exploration
AsiaInfo Technology: New Tech Exploration
Apr 9, 2026 · Artificial Intelligence

How OAG Shrinks a Million‑Token Ontology to 11% While Keeping LLM Reasoning Power

This article presents the OAG (Ontology‑Augmented Generation) architecture, which uses a three‑stage pipeline of semantic filtering, graph‑based path pruning, and format conversion to compress enterprise‑scale ontologies by up to 89% of tokens while limiting inference accuracy loss to around 3% and adding only ~240 ms latency.

AI agentsLLMOntology
0 likes · 21 min read
How OAG Shrinks a Million‑Token Ontology to 11% While Keeping LLM Reasoning Power
Data Party THU
Data Party THU
Aug 25, 2025 · Industry Insights

Can a New Algorithm Really Beat Dijkstra? Inside the Breakthrough Shortest‑Path Method

A new shortest‑path algorithm developed by researchers at Tsinghua University claims to overcome the long‑standing sorting bottleneck of Dijkstra’s classic method, extending to both undirected and directed graphs and sparking fresh debate on algorithmic optimality and future research directions.

Computational ComplexityDijkstraalgorithm breakthrough
0 likes · 10 min read
Can a New Algorithm Really Beat Dijkstra? Inside the Breakthrough Shortest‑Path Method
Baidu Geek Talk
Baidu Geek Talk
Jun 18, 2025 · Artificial Intelligence

How Graph Algorithms Power Anti‑Fraud in Marketing and E‑Commerce

This article explores how black‑market cheating in marketing campaigns and e‑commerce is detected using graph‑based techniques such as same‑person mining, label propagation, Fraudar, and GCN models, and discusses future directions like multimodal data fusion and real‑time dynamic graph computation.

FraudarGCNRisk Detection
0 likes · 18 min read
How Graph Algorithms Power Anti‑Fraud in Marketing and E‑Commerce
DataFunTalk
DataFunTalk
Aug 9, 2024 · Artificial Intelligence

Modeling User Propagation Ability for Social Recommendation and Influence Maximization in Games

This article presents a comprehensive study on leveraging user propagation ability metrics for friend recommendation and influence maximization in gaming environments, introducing a conversion‑funnel‑aware diffusion model, novel influence‑maximization variants, efficient greedy algorithms, and extensive offline and online experiments that demonstrate significant performance gains over traditional methods.

Gaminggraph algorithmsinfluence maximization
0 likes · 16 min read
Modeling User Propagation Ability for Social Recommendation and Influence Maximization in Games
JavaEdge
JavaEdge
Jul 13, 2024 · Databases

Mastering Neo4j: From Basics to Advanced Graph Queries and Performance Tuning

This article introduces Neo4j, explains its property‑graph model, demonstrates how to write and optimize Cypher queries, explores advanced features like full‑text search and built‑in graph algorithms, and showcases real‑world use cases and integration options for modern applications.

CypherFull‑Text SearchGraph Queries
0 likes · 10 min read
Mastering Neo4j: From Basics to Advanced Graph Queries and Performance Tuning
Python Programming Learning Circle
Python Programming Learning Circle
Jun 28, 2024 · Artificial Intelligence

Python 'communities' Library: Implementing Louvain, Girvan‑Newman, Hierarchical, Spectral, and Bron‑Kerbosch Community Detection Algorithms

This article introduces the open‑source Python library communities, which provides implementations of several community‑detection algorithms—including Louvain, Girvan‑Newman, hierarchical clustering, spectral clustering, and Bron‑Kerbosch—along with installation instructions, usage examples, and visualization tools for network analysis.

community-detectiongraph algorithmsmachine-learning
0 likes · 6 min read
Python 'communities' Library: Implementing Louvain, Girvan‑Newman, Hierarchical, Spectral, and Bron‑Kerbosch Community Detection Algorithms
Model Perspective
Model Perspective
Feb 1, 2024 · Operations

Essential Guide to Evaluation and Optimization Models: Concepts, Methods, and Algorithms

This article compiles recent resources on evaluation and optimization models, covering fundamental concepts, data preprocessing techniques, weighting methods such as TOPSIS and entropy, as well as linear and integer programming, graph theory, network algorithms, and meta‑heuristic approaches like simulated annealing and genetic algorithms.

Linear ProgrammingOperations Researchevaluation models
0 likes · 3 min read
Essential Guide to Evaluation and Optimization Models: Concepts, Methods, and Algorithms
JD Cloud Developers
JD Cloud Developers
Sep 20, 2023 · Artificial Intelligence

Unlocking Hidden Communities: A Deep Dive into Graph Community Detection Algorithms

This article explains the fundamentals of community detection in graph computing, contrasting it with clustering, describing key concepts such as modularity, and reviewing classic algorithms like Louvain, node2vec‑based methods, and Infomap, while highlighting their applications across domains.

Infomapcommunity-detectiongraph algorithms
0 likes · 11 min read
Unlocking Hidden Communities: A Deep Dive into Graph Community Detection Algorithms
JD Tech
JD Tech
Sep 12, 2023 · Fundamentals

Community Detection Algorithms: Concepts, Types, and Classic Methods

This article introduces community detection as a fundamental graph algorithm, explains its basic concepts and types, compares it with clustering, discusses evaluation metrics like modularity, and reviews classic methods such as Louvain, node2vec‑based approaches, and the information‑theoretic Infomap algorithm.

InfomapUnsupervised Learningcommunity-detection
0 likes · 13 min read
Community Detection Algorithms: Concepts, Types, and Classic Methods
DataFunTalk
DataFunTalk
Sep 7, 2023 · Artificial Intelligence

Ant Group's Knowledge Graph: Overview, Construction, Applications, and Integration with Large Models

This article presents Ant Group's work on knowledge graphs, covering the fundamentals, construction pipeline, fusion techniques, cognitive modeling, real‑world applications, and the emerging synergy between knowledge graphs and large language models, while highlighting technical challenges and future directions.

AIAnt GroupApplications
0 likes · 13 min read
Ant Group's Knowledge Graph: Overview, Construction, Applications, and Integration with Large Models
DataFunSummit
DataFunSummit
Aug 11, 2023 · Artificial Intelligence

Application of Knowledge Graphs in Risk Control at Wing Payment

This presentation details how Wing Payment leverages a large‑scale, multimodal knowledge graph and AI techniques—including computer vision, unsupervised and supervised learning, federated learning, and graph neural networks—to detect and mitigate fraud across payment, e‑commerce, and credit scenarios, while outlining system architecture, algorithmic approaches, case studies, and future research directions.

Financial ServicesMultimodal Datafraud detection
0 likes · 17 min read
Application of Knowledge Graphs in Risk Control at Wing Payment
DataFunSummit
DataFunSummit
Aug 2, 2023 · Big Data

Loop Detection in Risk Control: Challenges, Distributed Graph Computing Optimizations, and ArcNeural Engine Case Studies

This article discusses the challenges of loop detection in financial risk control, presents distributed graph computing optimization techniques—including pruning, multi‑graph handling, and memory‑efficient algorithms—shows experimental results, and shares real‑world ArcNeural engine case studies and future directions.

ArcNeuralBig DataLoop Detection
0 likes · 13 min read
Loop Detection in Risk Control: Challenges, Distributed Graph Computing Optimizations, and ArcNeural Engine Case Studies
Architect
Architect
Jun 19, 2023 · Backend Development

Design and Implementation of the Comet Workflow Engine

This article presents a comprehensive overview of the Comet workflow engine, detailing its background, architecture, key design concepts, graph‑based legality checks, plugin mechanisms, and practical use cases such as SRE automation, permission requests, and dynamic linear processes, illustrating how a flexible, low‑code platform can streamline enterprise business flows.

AutomationProcess Enginegraph algorithms
0 likes · 18 min read
Design and Implementation of the Comet Workflow Engine
DataFunSummit
DataFunSummit
Jun 18, 2023 · Artificial Intelligence

Intelligent Risk Control Forum – Sessions on Graph Algorithms, Pre‑trained GNN, Loop Detection, Active Learning, and Unstructured Data

The Intelligent Risk Control Forum gathers experts from Tencent, Huawei, Ant Group and academia to present the latest research on graph‑based algorithms, loop detection, pre‑trained graph neural networks, active learning and unstructured‑data risk models, addressing challenges such as data sparsity, adversarial behavior and model robustness.

Loop Detectiongraph algorithmsmachine learning
0 likes · 8 min read
Intelligent Risk Control Forum – Sessions on Graph Algorithms, Pre‑trained GNN, Loop Detection, Active Learning, and Unstructured Data
DataFunTalk
DataFunTalk
May 27, 2023 · Artificial Intelligence

Graph Algorithms in Alibaba E‑commerce Risk Control: Practices and Insights

The article presents a comprehensive overview of how graph algorithms are applied in Alibaba's e‑commerce risk control system, detailing six sections that include risk scenario introductions, interaction and product content risk methods, dynamic heterogeneous graph practices, a large‑scale competition, and future research directions.

Dynamic GraphRisk Detectione-commerce risk
0 likes · 18 min read
Graph Algorithms in Alibaba E‑commerce Risk Control: Practices and Insights
Network Intelligence Research Center (NIRC)
Network Intelligence Research Center (NIRC)
May 15, 2023 · Operations

How Delta-net Achieves Sub‑Millisecond Real‑Time Network Verification with Atoms

Delta-net introduces an interval‑based Atom model and a directed‑graph verification algorithm that enable sub‑millisecond, incremental detection of forwarding loops, blackholes, and reachability issues in large, complex networks, as demonstrated by microsecond‑scale performance tests on real hardware.

AtomsDelta-netPerformance Evaluation
0 likes · 6 min read
How Delta-net Achieves Sub‑Millisecond Real‑Time Network Verification with Atoms
Model Perspective
Model Perspective
Mar 23, 2023 · Fundamentals

Mastering Shortest Path Algorithms: Theory, Models, and Python NetworkX Example

This article explains the shortest path problem in graph theory, presents its integer linear programming model, reviews classic algorithms such as Dijkstra, Bellman‑Ford, and Floyd‑Warshall, and demonstrates solving a city‑flight cost example using Python’s NetworkX library with code snippets.

DijkstraLinear Programminggraph algorithms
0 likes · 7 min read
Mastering Shortest Path Algorithms: Theory, Models, and Python NetworkX Example
Baidu Geek Talk
Baidu Geek Talk
Mar 20, 2023 · Artificial Intelligence

How Graph Neural Networks Boost Anti‑Cheat in User Referral Activities

This article analyzes the use of graph neural network models, including GCN and multi‑graph SCGCN, to tackle cheating in referral‑based user acquisition by capturing user relationships, improving sample purity, and achieving up to a 50% increase in cheat‑sample recall.

GCNSCGCNanti-cheat
0 likes · 12 min read
How Graph Neural Networks Boost Anti‑Cheat in User Referral Activities
DataFunSummit
DataFunSummit
Jan 18, 2023 · Artificial Intelligence

Interview on the Current State, Challenges, and Future Trends of Graph Algorithms

This interview summarizes experts' insights on graph algorithm technology, covering its early industrial adoption, data scale and sparsity challenges, various graph types and models, application scenarios such as recommendation and risk control, R&D workflow hurdles, and emerging research directions like pre‑training, explainability, and combinatorial optimization.

ApplicationsFuture Trendsgraph algorithms
0 likes · 14 min read
Interview on the Current State, Challenges, and Future Trends of Graph Algorithms
DataFunSummit
DataFunSummit
Oct 13, 2022 · Artificial Intelligence

Merchant Security: Authenticity Recognition and Transaction Risk Identification Using AI Techniques

This article presents a comprehensive AI‑driven framework for merchant security that covers authenticity recognition through credential, text, and transaction analysis, advanced risk detection using self‑supervised, semi‑supervised, and graph‑based models, and intelligent decision‑making to balance risk mitigation with user experience.

AI risk detectiongraph algorithmsintelligent decision
0 likes · 17 min read
Merchant Security: Authenticity Recognition and Transaction Risk Identification Using AI Techniques
DataFunTalk
DataFunTalk
Aug 24, 2022 · Databases

Building Intelligent Supply Chains with Graph Databases and Knowledge Graphs

This article explains how the data challenges of modern intelligent supply chains can be addressed by using graph databases and knowledge graphs, detailing supply chain background, graph database fundamentals, graph algorithms, and real‑world case studies that illustrate risk assessment and logistics optimization.

Knowledge GraphNeo4jSupply Chain
0 likes · 18 min read
Building Intelligent Supply Chains with Graph Databases and Knowledge Graphs
Model Perspective
Model Perspective
Aug 20, 2022 · Fundamentals

Unlock SciPy’s Sparse Graph Algorithms: Shortest Paths, MSTs & More

This article lists the key SciPy sparse‑graph functions—such as connected components, Laplacian, various shortest‑path algorithms, traversals, minimum spanning tree, flow and matching utilities—and provides Python code examples demonstrating their use.

Pythongraph algorithmsminimum spanning tree
0 likes · 4 min read
Unlock SciPy’s Sparse Graph Algorithms: Shortest Paths, MSTs & More
Baidu Geek Talk
Baidu Geek Talk
Aug 16, 2022 · Artificial Intelligence

Louvain Algorithm for Community Detection in Anti‑Fraud Systems

The Louvain algorithm, a fast modularity‑maximizing community‑detection method, iteratively merges nodes into hierarchical super‑nodes, enabling anti‑fraud systems to uncover hidden collusive groups in weighted transaction graphs, thereby improving detection of fake orders, coupon abuse, and other illicit behaviors despite its iterative nature and streaming limitations.

anti-fraudcommunity-detectiongraph algorithms
0 likes · 10 min read
Louvain Algorithm for Community Detection in Anti‑Fraud Systems
DataFunTalk
DataFunTalk
Jul 23, 2022 · Artificial Intelligence

Graph Algorithm Deployment and Practices on the DataFun Security Spark Cluster

This article presents a comprehensive overview of deploying and running graph learning algorithms—both inductive and transductive—on the secure Spark cluster, covering framework choices, data sampling strategies, distributed training techniques, model evaluation metrics, and future directions.

Big DataDistributed TrainingSpark
0 likes · 13 min read
Graph Algorithm Deployment and Practices on the DataFun Security Spark Cluster
DataFunTalk
DataFunTalk
Jul 1, 2022 · Artificial Intelligence

Building and Scaling Ant Group's Merchant Knowledge Graph: Architecture, Construction, Fusion, and Open Platform

This article describes Ant Group's merchant knowledge graph, covering its background, overall architecture, data sources, schema design, processing pipeline, cross‑graph fusion techniques, cognitive applications such as vector‑based recall, and the upcoming open‑source and SDK initiatives aimed at sharing the knowledge and tools with the community.

AIKnowledge Graphgraph algorithms
0 likes · 20 min read
Building and Scaling Ant Group's Merchant Knowledge Graph: Architecture, Construction, Fusion, and Open Platform
DataFunSummit
DataFunSummit
May 23, 2022 · Artificial Intelligence

Applying Graph Machine Learning for Intelligent Anti‑Fraud: Models, Algorithms, and Real‑World Applications

This article explores how graph machine learning can be leveraged for intelligent anti‑fraud, covering business background, common fraud models and graph algorithm principles, practical deployment of graph algorithms, challenges in fraud modeling, and future research directions.

Graph Machine LearningRisk ModelingUnsupervised Learning
0 likes · 20 min read
Applying Graph Machine Learning for Intelligent Anti‑Fraud: Models, Algorithms, and Real‑World Applications
DataFunSummit
DataFunSummit
Sep 20, 2021 · Artificial Intelligence

Graph Algorithm Applications in Douyu Live Stream Anti‑Cheat: Architecture, Evolution, Modeling, and Case Studies

This article explains how Douyu leverages graph algorithms for live‑stream traffic anti‑cheat, detailing the platform’s risk scenarios, the overall graph architecture, its evolution, modeling workflow, practical case studies, and the resulting improvements in fraud detection and interpretability.

AIRisk Modelinganti-cheat
0 likes · 16 min read
Graph Algorithm Applications in Douyu Live Stream Anti‑Cheat: Architecture, Evolution, Modeling, and Case Studies
DataFunTalk
DataFunTalk
Jun 23, 2021 · Artificial Intelligence

Graph Algorithm Practices for Anti‑Cheat on the Douyu Live‑Streaming Platform

This article explains how Douyu uses graph‑based algorithms to detect and mitigate fraudulent streaming traffic, covering the platform's risk‑control scenarios, the overall graph architecture, its evolution, modeling workflow, practical case studies, and the resulting improvements in detection accuracy and interpretability.

anti-cheatgraph algorithmsgraph embedding
0 likes · 16 min read
Graph Algorithm Practices for Anti‑Cheat on the Douyu Live‑Streaming Platform
Intelligent Backend & Architecture
Intelligent Backend & Architecture
May 24, 2021 · Fundamentals

Master Data Structures & Algorithms: Comprehensive Guide with Visual Tools

This article provides a comprehensive overview of data structures and algorithms, covering fundamental concepts such as data, elements, objects, logical and storage structures, common structures like lists, stacks, queues, trees, graphs, algorithm design techniques, complexity analysis, and includes visual resources and code examples.

AlgorithmsData StructuresQueue
0 likes · 33 min read
Master Data Structures & Algorithms: Comprehensive Guide with Visual Tools
DataFunTalk
DataFunTalk
May 15, 2021 · Artificial Intelligence

Knowledge Graph Course Syllabus Overview

This teaching plan outlines a comprehensive Knowledge Graph course covering fundamentals, representation, storage, extraction, reasoning, fusion, question answering, graph algorithms, and emerging technologies across nine detailed chapters, including language integration, ontology matching, and multimodal extensions.

Knowledge Graphartificial intelligencecourse syllabus
0 likes · 4 min read
Knowledge Graph Course Syllabus Overview
Intelligent Backend & Architecture
Intelligent Backend & Architecture
May 14, 2021 · Fundamentals

Master Hash Tables, Heaps, and Graph Algorithms: From Basics to Dijkstra

This article introduces core data structures—hash tables, heaps, and graphs—explains their definitions, visual representations, and key operations, then delves into fundamental graph algorithms such as BFS, Dijkstra, Floyd, minimum spanning trees, and topological sorting, illustrating each with examples and code.

Data StructuresDijkstraHeap
0 likes · 16 min read
Master Hash Tables, Heaps, and Graph Algorithms: From Basics to Dijkstra
DataFunTalk
DataFunTalk
Jan 31, 2021 · Artificial Intelligence

Applying Graph Algorithms and Graph Convolutional Networks for Advertising Anti‑Fraud at 58.com

This article explains how various graph algorithms—including connected components, label propagation, Louvain community detection, and Graph Convolutional Networks—are built on large‑scale user‑behavior graphs using Spark GraphX to detect and mitigate advertising fraud, detailing methodology, implementation, and experimental results.

GCNSpark GraphXanti-fraud
0 likes · 13 min read
Applying Graph Algorithms and Graph Convolutional Networks for Advertising Anti‑Fraud at 58.com
Sohu Tech Products
Sohu Tech Products
Jan 20, 2021 · Artificial Intelligence

Graph Algorithm Design and Optimization for Detecting Black‑Market Users in Virtual Networks

This article presents a comprehensive study on using graph representation learning, particularly GraphSAGE and its optimizations, to identify and mitigate black‑market accounts in virtual networks, covering background, algorithm design, handling isolated nodes and heterogeneity, and evaluation results.

GraphSAGEblack market detectiongraph algorithms
0 likes · 13 min read
Graph Algorithm Design and Optimization for Detecting Black‑Market Users in Virtual Networks
DataFunTalk
DataFunTalk
Jan 18, 2021 · Artificial Intelligence

Graph Algorithm Design and Optimization for Detecting Black Market Users in Virtual Networks

This article presents a comprehensive overview of using graph representation learning and clustering, particularly GraphSAGE and its optimizations, to identify and mitigate black‑market (malicious) accounts in virtual networks, discussing background, objectives, challenges such as isolation and heterogeneity, and evaluation results.

GraphSAGEIsolationblack market detection
0 likes · 13 min read
Graph Algorithm Design and Optimization for Detecting Black Market Users in Virtual Networks
DataFunSummit
DataFunSummit
Dec 9, 2020 · Artificial Intelligence

Construction and Application of Financial Knowledge Graphs: AI Key Technologies, Building Practices, and Real‑World Use Cases

This article explains how financial institutions can leverage massive structured and unstructured data by building a financial knowledge graph, detailing AI core technologies, schema design, extraction methods, storage solutions, and a range of practical applications such as intelligent tagging, recommendation, policy analysis, and executive relationship mining.

Information ExtractionKnowledge Graphartificial intelligence
0 likes · 16 min read
Construction and Application of Financial Knowledge Graphs: AI Key Technologies, Building Practices, and Real‑World Use Cases
JD Tech Talk
JD Tech Talk
Dec 3, 2020 · Artificial Intelligence

Graph Algorithms and Graph Neural Networks for Fraud Detection

The article explains how building account relationship graphs and applying both traditional graph algorithms and modern graph neural networks—through community detection, anomaly detection, semi‑supervised and unsupervised learning, and dynamic graph techniques—can effectively identify and dismantle fraud groups in online services.

AISemi-supervised Learningdynamic graphs
0 likes · 11 min read
Graph Algorithms and Graph Neural Networks for Fraud Detection
58 Tech
58 Tech
Nov 18, 2020 · Artificial Intelligence

Applying Graph Algorithms and Graph Convolutional Networks to Advertising Anti‑Fraud

This article describes how graph theory and graph convolutional neural networks are leveraged to model user‑IP relationships, detect fraudulent advertising clusters, and improve detection accuracy and recall through a combination of unsupervised graph algorithms and supervised GCN training in a large‑scale ad‑anti‑fraud system.

AdvertisingGCNSpark GraphX
0 likes · 14 min read
Applying Graph Algorithms and Graph Convolutional Networks to Advertising Anti‑Fraud
Laravel Tech Community
Laravel Tech Community
Oct 14, 2020 · Fundamentals

Ten Fundamental Algorithms: Sorting, Searching, Graph Traversal, and More

This article introduces ten essential algorithms—including Quick Sort, Heap Sort, Merge Sort, Binary Search, BFPRT, Depth‑First Search, Breadth‑First Search, Dijkstra's shortest‑path, Dynamic Programming, and Naive Bayes—explaining their principles, typical use cases, and step‑by‑step procedures.

AlgorithmsSearchSorting
0 likes · 12 min read
Ten Fundamental Algorithms: Sorting, Searching, Graph Traversal, and More
DataFunTalk
DataFunTalk
Aug 19, 2020 · Artificial Intelligence

Fraudar: Graph-Based Fraud Detection in E‑commerce Transaction Networks

The article presents a comprehensive overview of e‑commerce fraud, especially brush‑order schemes, and introduces the Fraudar algorithm—a graph‑based unsupervised method that leverages bipartite network analysis, global suspiciousness metrics, priority‑tree optimization, and collaborative supervised training to efficiently identify dense fraudulent sub‑graphs.

FraudarUnsupervised Learningbipartite graph
0 likes · 15 min read
Fraudar: Graph-Based Fraud Detection in E‑commerce Transaction Networks
JD Tech Talk
JD Tech Talk
Aug 7, 2020 · Information Security

Fraudar: Graph-Based Fraud Detection in Bipartite Transaction Networks

The article explains how e‑commerce fraud such as fake order brushing can be modeled as a bipartite transaction network and tackled with the Fraudar algorithm, which iteratively removes low‑suspicion nodes using a global suspiciousness metric and priority‑tree structures to uncover dense suspicious sub‑graphs.

Unsupervised Learningbipartite graphe‑commerce
0 likes · 14 min read
Fraudar: Graph-Based Fraud Detection in Bipartite Transaction Networks
58 Tech
58 Tech
Mar 26, 2020 · Big Data

LPA-Detector: Distributed Label Propagation with Confidence Weights for Large‑Scale Graph Risk Detection

The article introduces LPA-Detector, an open‑source project that redesigns the Label Propagation Algorithm using Spark GraphX to add node confidence weights and relationship influence, achieving significant improvements in execution efficiency and detection accuracy for massive graph data in risk‑control scenarios.

Big DataRisk DetectionSpark
0 likes · 8 min read
LPA-Detector: Distributed Label Propagation with Confidence Weights for Large‑Scale Graph Risk Detection
Alibaba Cloud Developer
Alibaba Cloud Developer
May 22, 2019 · Artificial Intelligence

Mastering Anomaly Detection: From Moving Averages to Isolation Forests

This comprehensive guide explores a wide range of anomaly detection techniques—including time‑series methods, statistical models, distance‑based approaches, tree‑based isolation forests, graph algorithms, behavior‑sequence Markov models, and supervised machine‑learning models—detailing their principles, formulas, and practical scenarios for detecting outliers in advertising, fraud, and system monitoring.

Isolation ForestTime Seriesanomaly detection
0 likes · 19 min read
Mastering Anomaly Detection: From Moving Averages to Isolation Forests
DataFunTalk
DataFunTalk
Apr 28, 2019 · Artificial Intelligence

Graph Algorithms for Fraud Detection and Community Detection: Modularity, Louvain, Infomap, node2vec and comE

This article explains how graph‑based algorithms such as centrality measures, modularity optimization, Louvain, Infomap, node2vec and the comE framework can be applied to financial fraud detection and community discovery, detailing their principles, formulas, implementation steps and evaluation metrics.

Infomapcommunity-detectionfraud detection
0 likes · 14 min read
Graph Algorithms for Fraud Detection and Community Detection: Modularity, Louvain, Infomap, node2vec and comE
21CTO
21CTO
Sep 26, 2017 · Big Data

How NTE Algorithm Accelerates New Common‑Friend Discovery in Billion‑Scale Graphs

Introducing the NTE (New Triangle Enumeration) algorithm, a divide‑and‑conquer approach that transforms the computation of newly added common friends in massive social graphs into efficient triangle enumeration tasks, with detailed implementations using GraphX‑based GTE, join‑based JTE, and sort‑based STE methods.

GraphXSocial Network AnalysisSpark
0 likes · 12 min read
How NTE Algorithm Accelerates New Common‑Friend Discovery in Billion‑Scale Graphs
ITPUB
ITPUB
Apr 24, 2017 · Fundamentals

Essential Algorithm Cheat Sheet: Sorting, Graph, Greedy, and Bit Operations

This article provides a concise reference of core algorithms—including sorting methods, graph traversals, shortest‑path techniques, greedy strategies, bit‑level manipulations, and asymptotic notation—detailing their stability, time complexities, key concepts, and practical examples for interview preparation.

AlgorithmsInterview PreparationSorting
0 likes · 8 min read
Essential Algorithm Cheat Sheet: Sorting, Graph, Greedy, and Bit Operations
Qunar Tech Salon
Qunar Tech Salon
Mar 29, 2016 · Fundamentals

Overview of Ten Classic Algorithms: Sorting, Searching, Graph Traversal, and Machine Learning

This article presents concise explanations and step‑by‑step procedures for ten classic algorithms—including quick sort, heap sort, merge sort, binary search, BFPRT selection, depth‑first and breadth‑first graph traversals, Dijkstra’s shortest‑path method, dynamic programming principles, and the Naive Bayes classifier—highlighting their complexities and core ideas.

SearchingSortingalgorithm fundamentals
0 likes · 11 min read
Overview of Ten Classic Algorithms: Sorting, Searching, Graph Traversal, and Machine Learning
21CTO
21CTO
Feb 29, 2016 · Fundamentals

Master 10 Essential Algorithms: From QuickSort to Naive Bayes

This article presents concise explanations, step‑by‑step procedures, and visual illustrations for ten core algorithms—including QuickSort, HeapSort, MergeSort, Binary Search, BFPRT, DFS, BFS, Dijkstra, Dynamic Programming, and Naive Bayes—highlighting their principles, complexities, and typical use cases.

Search AlgorithmsSorting Algorithmsdynamic programming
0 likes · 15 min read
Master 10 Essential Algorithms: From QuickSort to Naive Bayes