LIME: Low-Cost and Incremental Learning for Dynamic Heterogeneous Information Networks

Understanding the interconnected relationships of large-scale information networks like social, scholar and Internet of Things networks is vital for tasks like recommendation and fraud detection. The vast majority of the real-world networks are inherently heterogeneous and dynamic, containing many different types of nodes and edges and can change drastically over time. The dynamicity and heterogeneity make it extremely challenging to reason about the network structure. Unfortunately, existing approaches are inadequate in modeling real-life networks as they require extensive computational resources and do not scale well to large, dynamically evolving networks. We introduce LIME, a better approach for modeling dynamic and heterogeneous information networks. LIME is designed to extract high-quality network representation with significantly lower memory resources and computational time over the state-of-the-art. Unlike prior work that uses a vector to encode each network node, we exploit the semantic relationships among network nodes to encode multiple nodes with similar semantics in shared vectors. We evaluate LIME by applying it to three representative network-based tasks, node classification, node clustering and anomaly detection, performing on three large-scale datasets. Our extensive experiments demonstrate that LIME not only reduces the memory footprint by over 80\% and computational time over 2x when learning network representation but also delivers comparable performance for downstream processing tasks.