Bipartite Graph Neural Network for Transaction Data Embedding
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Solution Overview
Problem
Existing methods for unsupervised representation learning in bipartite graphs face challenges due to high variance in nodes and edges, as well as the need to learn from dissimilar domains, where nodes from one partition cannot directly connect to nodes of the same partition, limiting the effectiveness of existing approaches.
Innovation Solution
The implementation of a bipartite graph neural network (BipGNN) model that samples direct and skip neighbor nodes using a neighborhood sampling method, aggregates information through direct and skip neighborhood aggregation, and optimizes node representations using a dual loss function to capture topological information and mutual information, enabling the learning of comprehensive node embeddings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing representation learning methods are applied to bipartite graphs, then the learning process can be performed, but the effectiveness is limited due to high variance in nodes and edges and heterogeneous nature of the graph
Solution Approach 1:
The patent segments the neighborhood aggregation process into two distinct components: direct neighborhood aggregation (aggregating from immediate neighbors) and skip neighborhood aggregation (aggregating from neighbors of neighbors). This segmentation allows the model to handle the heterogeneous nature of bipartite graphs by treating different neighborhood types differently, thereby improving representation learning effectiveness while managing complexity through structured decomposition
Solution Approach 2:
The patent introduces skip neighbor nodes as an intermediary mechanism to bridge the limitation of direct connections in bipartite graphs. By aggregating information from skip neighbors (nodes at distance 2), the model indirectly captures same-partition node information through the other partition, effectively mediating the constraint that nodes from one partition cannot directly connect to nodes of the same partition
2Measurement precision
If direct neighborhood aggregation is used only, then the computation is simpler, but the node representations are less accurate due to limited information from same-partition nodes
Solution Approach 1:
The aggregation process is segmented into direct and skip components, each with its own aggregation function. Direct neighborhood aggregation captures immediate neighbor information, while skip neighborhood aggregation captures indirect information from same-partition nodes. This segmentation enables accurate node representations by combining both information sources while maintaining manageable complexity through modular aggregation functions
Solution Approach 2:
The patent merges the outputs of direct neighborhood aggregation and skip neighborhood aggregation to form the final node representation. By combining these two aggregation results, the model achieves comprehensive node embeddings that incorporate both direct neighbor information and indirect same-partition node information, thereby improving representation accuracy while maintaining computational efficiency through unified aggregation operations
3Reliability
If more neighborhood information is aggregated, then the node representations become more comprehensive, but the computational time increases
Solution Approach 1:
The patent applies partial action by sampling a subset of neighbor nodes and skip neighbor nodes rather than aggregating from all possible neighbors. This sampling approach provides sufficient neighborhood information for comprehensive node representations while significantly reducing computational time and resources required for the aggregation process
Solution Approach 2:
The computational process is segmented into separate direct and skip aggregation stages, allowing independent optimization of each. By processing direct neighbors and skip neighbors through distinct aggregation functions, the model achieves comprehensive representations efficiently, avoiding the computational overhead of a monolithic aggregation approach that would process all neighborhood information uniformly
Data Source
AI summary
Embodiments provide methods and systems for unsupervised representation learning for bipartite graphs. Method performed by server system includes accessing historical transaction data from database. Method includes generating a bipartite graph based on historical transaction data. Bipartite graph represents a computer-based graph representation of a plurality of cardholders as first nodes and a plurality of merchants as second nodes and payment transactions between first nodes and second nodes as edges. Method includes sampling direct neighbor nodes and skip neighbor nodes associated with a node based on neighborhood sampling method and executing direct neighborhood aggregation method and skip neighborhood aggregation method to obtain direct neighborhood embedding and skip neighborhood embedding associated with node, respectively. Method includes optimizing combination of direct and skip neighborhood embeddings for obtaining final node representation associated with the node and executing graph context prediction tasks based on final node representations of first nodes and second nodes.