SENSE: Semantically Enhanced Node Sequence Embedding

Rallapalli, Swati; Ma, Liang; Srivatsa, Mudhakar; Swami, Ananthram; Kwon, Heesung; Bent, Graham; Simpkin, Christopher

doi:10.1109/bigdata47090.2019.9006591

Cited by 2 publications

(1 citation statement)

References 21 publications

(18 reference statements)

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“…For example, work in Simpkin et al (2017) used vector symbolic architecture for representing and orchestrating complex decentralized workflows. Work in Rallapalli et al (2019) developed a novel embedding mechanism for single graph nodes that co-learns graph structure and textual descriptions. A key HDC advantage is its training capability in one or few shots, where object categories are learned from one or few examples and in a single pass over the training data instead of many iterations.…”

Section: Preliminarymentioning

confidence: 99%

GrapHD: Graph-Based Hyperdimensional Memorization for Brain-Like Cognitive Learning

et al. 2022

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Memorization is an essential functionality that enables today's machine learning algorithms to provide a high quality of learning and reasoning for each prediction. Memorization gives algorithms prior knowledge to keep the context and define confidence for their decision. Unfortunately, the existing deep learning algorithms have a weak and nontransparent notion of memorization. Brain-inspired HyperDimensional Computing (HDC) is introduced as a model of human memory. Therefore, it mimics several important functionalities of the brain memory by operating with a vector that is computationally tractable and mathematically rigorous in describing human cognition. In this manuscript, we introduce a brain-inspired system that represents HDC memorization capability over a graph of relations. We propose GrapHD, hyperdimensional memorization that represents graph-based information in high-dimensional space. GrapHD defines an encoding method representing complex graph structure while supporting both weighted and unweighted graphs. Our encoder spreads the information of all nodes and edges across into a full holistic representation so that no component is more responsible for storing any piece of information than another. Then, GrapHD defines several important cognitive functionalities over the encoded memory graph. These operations include memory reconstruction, information retrieval, graph matching, and shortest path. Our extensive evaluation shows that GrapHD: (1) significantly enhances learning capability by giving the notion of short/long term memorization to learning algorithms, (2) enables cognitive computing and reasoning over memorization graph, and (3) enables holographic brain-like computation with substantial robustness to noise and failure.

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Section: Preliminarymentioning

confidence: 99%

GrapHD: Graph-Based Hyperdimensional Memorization for Brain-Like Cognitive Learning

et al. 2022

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SENSE: Semantically Enhanced Node Sequence Embedding

Rallapalli

Srivatsa

et al. 2019

2019 IEEE International Conference on Big Data (Big Data)

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Effectively capturing graph node sequences in the form of vector embeddings is critical to many applications. We achieve this by (i) first learning vector embeddings of single graph nodes and (ii) then composing them to compactly represent node sequences. Specifically, we propose SENSE-S (Semantically Enhanced Node Sequence Embedding -for Single nodes), a skip-gram based novel embedding mechanism, for single graph nodes that co-learns graph structure as well as their textual descriptions. We demonstrate that SENSE-S vectors increase the accuracy of multi-label classification tasks by up to 50% and link-prediction tasks by up to 78% under a variety of scenarios using real datasets. Based on SENSE-S, we next propose generic SENSE to compute composite vectors that represent a sequence of nodes, where preserving the node order is important. We prove that this approach is efficient in embedding node sequences, and our experiments on real data confirm its high accuracy in node order decoding.

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SENSE: Semantically Enhanced Node Sequence Embedding

Cited by 2 publications

References 21 publications

GrapHD: Graph-Based Hyperdimensional Memorization for Brain-Like Cognitive Learning

GrapHD: Graph-Based Hyperdimensional Memorization for Brain-Like Cognitive Learning

SENSE: Semantically Enhanced Node Sequence Embedding

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