graph algorithms

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

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.

Pythoncommunity-detectiongraph algorithms

0 likes · 6 min read

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

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 Programmingevaluation modelsgraph algorithms

0 likes · 3 min read

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

Cognitive Technology Team

Nov 12, 2023 · Fundamentals

Topological Sorting of Directed Acyclic Graphs with Java Implementation Using Guava

This article explains the definition and properties of directed acyclic graphs (DAG), describes the basic topological sorting algorithm steps, and provides a complete Java implementation using Guava's MutableGraph class, illustrating the process with an example and its execution result.

DAGGuavaJava

0 likes · 4 min read

Topological Sorting of Directed Acyclic Graphs with Java Implementation Using Guava

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.

InfomapLouvaincommunity-detection

0 likes · 13 min read

Community Detection Algorithms: Concepts, Types, and Classic Methods

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 GroupLarge Models

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

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.

Artificial Intelligencefinancial servicesfraud detection

0 likes · 17 min read

Application of Knowledge Graphs in Risk Control at Wing Payment

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 Datadistributed computing

0 likes · 13 min read

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

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.

Automationbackendgraph algorithms

0 likes · 18 min read

Design and Implementation of the Comet Workflow Engine

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.

Artificial Intelligencegraph algorithmsloop detection

0 likes · 8 min read

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

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 GraphGraph Neural Networkse-commerce risk

0 likes · 18 min read

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

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 ProgrammingNetworkX

0 likes · 7 min read

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

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.

Future TrendsGraph Neural Networksapplications

0 likes · 14 min read

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

Model Perspective

Jan 13, 2023 · Artificial Intelligence

Master Classic Modeling with Python: LP, Graphs, Clustering, PCA & More

This article presents Python implementations of classic mathematical modeling techniques—including linear programming with PuLP, shortest‑path analysis using NetworkX, K‑means and hierarchical clustering, principal component analysis, frequent‑pattern mining with FP‑Growth, and linear regression and K‑nearest‑neighbors—providing code snippets, explanations, and visualizations to guide readers through each method.

ClusteringFrequent Pattern MiningOptimization

0 likes · 12 min read

Master Classic Modeling with Python: LP, Graphs, Clustering, PCA & More

DataFunTalk

Dec 12, 2022 · Artificial Intelligence

Graph Algorithms in Risk Control: Fundamentals, Evolution, Platforms, and Future Outlook

This article presents a comprehensive overview of how graph algorithms and graph neural networks are applied to internet risk control, covering basic concepts, evolutionary trends, platform implementations, future challenges, and a Q&A session that bridges theory and practice.

Big DataGraph Neural Networksgraph algorithms

0 likes · 19 min read

Graph Algorithms in Risk Control: Fundamentals, Evolution, Platforms, and Future Outlook

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

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.

Neo4jgraph algorithmsgraph database

0 likes · 18 min read

Building Intelligent Supply Chains with Graph Databases and Knowledge Graphs

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.

PythonSciPygraph algorithms

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

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.

Louvainanti-fraudcommunity-detection

0 likes · 10 min read

Louvain Algorithm for Community Detection in Anti‑Fraud Systems

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 DataSparkdistributed training

0 likes · 13 min read

Graph Algorithm Deployment and Practices on the DataFun Security Spark Cluster