--We propose a multilayered symbolic framework for general graph classification that leverages sparse binary hypervectors and Tsetlin Machines (TMs). Each graph is encoded through structured message passing, where node, edge, and attribute information are bound and bundled into a symbolic hypervector . This process preserves the hierarchical semantics of the graph through layered binding--from node attributes to edge relations to structural roles--resulting in a compact, discrete representation. We also formulate a local interpretability framework which lends itself to a key advantage of our approach being locally interpretable: predictions can be traced back to specific nodes and edges by decoding their influence in the bundled representation. We validate our method on TUDataset benchmarks, demonstrating competitive accuracy with strong symbolic transparency compared to neural graph models. Graph classification is a fundamental task in graph-based machine learning, where the goal is to assign a label or predict a target for an entire graph.

Following the general theoretical framework of VSA (Vector Symbolic Architecture), a cognitive model with the use of sparse binary hypervectors is proposed. In addition, learning algorithms are introduced to bootstrap the model from incoming data stream, with much improved transparency and efficiency. Mimicking human cognitive process, the training can be performed online while inference is in session. Word-level embedding is re-visited with such hypervectors, and further applications in the field of NLP (Natural Language Processing) are explored.