Higher Order Knowledge Mining for Humanistic Data-Generated Graphs

Humanistic link oriented data (LOD) play a crucial role as they appear in technological and financial sectors including smart cities, social networks, and long supply chains. The primary difficulties in mining LOD are their volume, the rate of missing or corrupted values, the non-linear relationships, and its higher order nature. Therefore, deep learning approaches of comparable complexity should be used if meaningful knowledge is to be extracted from the LOD ocean. Tensor stack networks (TSNs) consist of layers with information flowing serially both between layers and within the same layer. TSNs natively handle multidimensional data such as images and graphs, preventing information loss from vectorization by preserving topological knowledge. Two TSNs have been implemented, one assessing the structural resilience of a graph based on connectivity patterns and one computing the correlation coefficient for graphs compressed with DCT2. In both cases TSNs achieved superior performance to baselines. Self organizing maps (SOMs) are unsupervised neural network architectures learning how to modify connections between two spaces, the data and the coordinate space. SOMs construct a low dimensional projection from the former to the latter while preserving up to an extent the primary topological properties. A tensor distance metric allows considerably more flexibility compared to vector-based ones as corroborated by clustering neuroimages from an open fMRI dataset. Graph neural networks (GNNs) have emerged as a versatile architectural paradigm. Through message passing and aggregation a global state eventually emerges from local ground truth stored at the vertices or at the edges. Research focuses on the conditions for the emergence of the steady state and the convergence rate. A GNN for discovering affective Twitter communities is proposed. Experiments on benchmark graphs suggest GNNs are more efficient than the baseline. Comparing the abovementioned architectures it follows that GNNs are the most flexible as they learn in an unsupervised and distributed way and they operate on irregular domains. However, they require a tensor distance metric to handle multidimensional data. Another conclusion is that GNNs offer higher explainability as they adjust to the underlying domain by naturally expressing its relationships no matter how complex they are without imposing any external structure on it.