MIT Technology Review

This is the most misunderstood graph in AI

To some, METR’s “time horizon plot” indicates that AI utopia—or apocalypse—is close at hand. The truth is more complicated.

Using complex networks to tame combustion instability

Engineers have long battled a problem that can cause loud, damaging oscillations inside gas turbines and aircraft engines: ...
Nature

c-MYC interacts with INI1/hSNF5 and requires the SWI/SNF complex for transactivation function

Figure 1: INI1 interacts with c-MYC in the two-hybrid system. Figure 2: INI1 directly interacts with c-MYC in vitro. Figure 3: Co-immunoprecipitation of c-MYC with INI1. Figure 4: Dominant-negative ...
IEEE

Causal Disentangled Graph Neural Network for Fault Diagnosis of Complex Industrial Process

Abstract: Graph neural networks (GNNs) are good at capturing the intricate topologies and dependencies among components and are outstanding in fault diagnosis tasks of complex industrial process. Bias ...
IEEE

Hierarchical Causal Graph Neural Networks With Cascading Failure Analysis for Complex Systems Root Cause Identification

Abstract: Modeling the evolution process of industrial cascading faults by capturing causal relationships has achieved widespread attention in the attack detection and security control field. However, ...
GitHub

Node Identifiers: Compact, Discrete Representations for Efficient Graph Learning (ICLR 2025)

Tested with Python 3.7, PyTorch 1.12.1, and PyTorch Geometric 2.3.1, dgl 1.0.2. pip install pandas pip install scikit_learn pip install numpy pip install scipy pip install einops pip install ogb pip ...