๐๐ง๐ฅ๐๐๐ฌ๐ก๐ข๐ง๐ ๐ญ๐ก๐ ๐๐จ๐ฐ๐๐ซ ๐จ๐ ๐๐ซ๐๐ฉ๐ก ๐๐ง๐๐ฅ๐ฒ๐ญ๐ข๐๐ฌ: ๐๐ ๐๐จ๐ฉ ๐๐ฒ๐ญ๐ก๐จ๐ง ๐๐ข๐๐ซ๐๐ซ๐ข๐๐ฌ, ๐๐ฅ๐ ๐จ๐ซ๐ข๐ญ๐ก๐ฆ๐ฌ, ๐๐ฒ๐ฉ๐๐ฌ ๐๐ง๐ ๐๐๐๐ก๐ง๐ข๐ช๐ฎ๐๐ฌ
Graph Analytics extracts valuable insights from complex, interconnected data with ability to represent relationships between entities.
๐พ๐ฃ๐๐ก๐ โ๐ ๐๐ก๐ ๐ค๐๐ฅ๐๐ ๐:
- Nodes: represent entities
- Edges: link between entities
๐พ๐ ๐๐๐ค ๐ ๐ ๐พ๐ฃ๐๐ก๐ ๐ธ๐๐๐๐ช๐ฅ๐๐๐ค:
*อ Identify key entities and their relationships *อ Discover patterns and anomalies in large-scale datasets *อ Generate recommendations and predictions based on past behavior *อ Uncover community structures within networks *อ Predict missing links and uncover hidden connections
๐๐ช๐ก๐๐ค ๐ ๐ ๐พ๐ฃ๐๐ก๐ ๐ธ๐๐๐๐ช๐ฅ๐๐๐ค:
๐๐ซ๐๐ฉ๐ก ๐๐๐ฎ๐ซ๐๐ฅ ๐๐๐ญ๐ฐ๐จ๐ซ๐ค๐ฌ (๐๐๐): A class of deep learning models that operate directly on graph structures.
Examples of GNNs include: โ Graph Convolutional Networks (GCN) โ Graph Attention Networks (GAT) โ GraphSAGE
๐ ๐๐๐ญ๐ฎ๐ซ๐๐ฌ ๐๐ฑ๐ญ๐ซ๐๐๐ญ๐ข๐จ๐ง ๐ฐ๐ข๐ญ๐ก ๐๐๐ง๐ญ๐ซ๐๐ฅ๐ข๐ญ๐ฒ ๐๐๐๐ฌ๐ฎ๐ซ๐๐ฌ: Centrality measures aim to identify the most important nodes in a graph.
Some examples include: โ Degree โ Betweenness โ Eigenvector โ PageRank โ Katz
๐๐ฅ๐ฎ๐ฌ๐ญ๐๐ซ๐ข๐ง๐ : Aim to group nodes into clusters based on their structural similarity.
Some examples include: โ Girvan-Newman โ Markov Cluster (MCL) โ Hierarchical agglomerative clustering (HAC)
๐๐ข๐ง๐ค ๐๐ซ๐๐๐ข๐๐ญ๐ข๐จ๐ง: Aim to predict missing links in a graph.
Some examples include: โ Louvain โ Infomap โ Walktrap
๐๐จ๐ฆ๐ฆ๐ฎ๐ง๐ข๐ญ๐ฒ ๐๐๐ญ๐๐๐ญ๐ข๐จ๐ง: Aim to identify groups of nodes that are densely connected within themselves but sparsely connected with the rest of the network.
Some examples include: โ Girvan-Newman โ Clauset-Newman-Moore โ Label Propagation โ Walktrap โ Fastgreedy
๐พ๐ฃ๐๐ก๐ ๐ธ๐๐๐๐ช๐ฅ๐๐๐ค ๐๐๐๐๐๐๐ข๐ฆ๐๐ค:
โ Graph Traversal: Visit every node in a graph, typically in a systematic order.
โ Shortest Path: Aim to find the shortest path between two nodes in a graph.
โ Connected Components: Identify groups of nodes that are all connected to each other.
โ Minimum Spanning Tree: Find the minimum set of edges needed to connect all nodes in a graph.
โ Maximum Flow: Find the maximum amount of flow that can pass through a graph, given constraints on the edges.
๐๐ ๐๐ฒ๐ญ๐ก๐จ๐ง ๐๐ข๐๐ซ๐๐ซ๐ข๐๐ฌ ๐ ๐๐จ๐ฎ๐ง๐:
๐NetworkX ๐ igraph ๐ karateclub ๐ graph-tool ๐ SNAP.py ๐ Deep Graph Library (DGL) ๐ PyTorch Geometric ๐ Spektral ๐ stellargraph ๐ scikit-network ๐ CDlib ๐ leidenalg ๐ markov-clustering ๐ pyclustering ๐ Graphein ๐ nxviz ๐ Grakn ๐ Tulip ๐ PowerGraph ๐ Gephi ๐ PyG ๐ Python-I graph ๐ NetworKit ๐ Grakel ๐ PyGraphistry
DGraph-Fin is a directed, unweighted dynamic graph that represents a social network among users of Finvolution Group. In this graph, a node represents a Finvolution user, and an edge from one user to another means that the user regards the other user as the emergency contact person Label: To better understand real-world financial scenarios, we classify the nodes as foreground nodes and background nodes. Foreground nodes are the ones that are labeled as normal (Class 0) and fraud (Class 1), which are also the nodes of our prediction task. Background nodes, on the other hand, are irrelevant to the task but play an important role in maintaining the connectivity of the graph.
Task: The task of DGraph-Fin is to detect fraudulent users based on node features and graph structural information. This is a common task in financial scenarios. We randomly split the nodes into training/validation/test sets with a ratio of 70:15:15.
Evolving pattern: Each edge in DGraph-Fin contains time information representing when the user filled in that emergency contact. To protect privacy, an encrypted timestamp is used to represent the time
