Zihe Zhou

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Publications

One arc —— Structural Reliability Scalability Selective Observation Interpretability

My work circles a single question: how do graph learning systems stay reliable at real scale, under limited observation, and with complex semantics?

Graphs
for a more
reliable
intelligent world
01
Structural Reliability
Triad
02
Scalability
Highway
03
Selective Observation
Scout
04
Interpretability
DSEG
Origin

Triad

Structural Reliability

Node, edge and community constraints in one program, suppressing the structural degeneracy that overlapping community detection falls into.

Zihe Zhou, Samin Aref · Triad: Suppressing Structural Degeneracy in Overlapping Community Detection

WAW 2026 Talk video Slides
Flagship
Scaling
reliable
communities

Highway

Scaling Reliable Structure

Scaling sparse structural backbones to large networks, for overlapping community detection that stays both reliable and fast.

Zihe Zhou, Samin Aref · Overlapping Network Community Detection Using Sparse Backbones

1.13M nodes · ASONAM 2026 · SNAM extension in preparation arXiv Benchmark cdlib
Independent
A model
you can
read

DSEG

Interpretable Generation on a Graph

DSEG-Char: a character-level poetry model where every character is a node in a 5,000-node graph. Writing a line is a walk over it, and each step reports which term — state, edge or context — selected that character.

Character-level model · 5,000 nodes · 1.24M parameters CodeReleased 2026 · single-authored · MIT
Independent

Scout

Learning What Is Worth Observing

Learning which stale graph information is worth refreshing, so a limited observation budget lifts downstream task performance.

Zihe Zhou · Budgeted Task-Aware Acquisition of Dynamic Networks

Dynamic Graphs · Task-aware Acquisition arXivCodePreprint 2026 · single-authored
From structure to understanding

“Graph learning systems that are more reliable let complex relationships be seen, understood, and trusted.”

Zihe Zhou