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aientity · one source so far

graph neural networks

for ml engineers, graph researchers

Steadyarxiv
Signal score
11
as of 35d ago
Live items
1
Trajectory
⏳ Too early
needs a few more snapshots

Why this scored 11

every term, weighted
Velocity+0.0 / 40

Its fastest-moving item, measured against the pace of its own source

Acceleration+11.2 / 25

Whether that velocity is itself speeding up, as a per-hour rate

Cross-source spread+0.0 / 25

How many independent communities its own items come from

Recency+1.4 / 10

Decays to zero over 14 days, counted from when we first saw it

Saturation penalty−1.6 / 30

Subtracted once something is big and old — sized by its biggest item, aged from when we first saw it

Composite11.0

Weights are hand-tuned, not learned — we're calibrating them against realized trends as history accumulates. On an entity's first sighting there's no previous reading to compare against, so acceleration starts from a neutral prior rather than a measurement, and velocity falls back to engagement over its whole lifetime until a second reading exists. Full methodology

Signal history

7-day window (free)
Not enough history yet — the trend line fills in as snapshots accrue (every 30 min).

The evidence

The live items this entity's score aggregates — every community independently talking about it right now. This is the corroboration, shown, not claimed.

  1. 1
    50

    Graph Neural Networks for RFID-Based Spatial Geometry Inference in Spatial AI Systems

    Researchers propose graph neural networks for RFID-based spatial geometry inference in spatial AI systems. · for ai researchers, spatial AI engineers

    arxivSteadyai92d ago
  2. 2
    19

    Not Just Oversmoothing: Detecting the Echo Chamber Effect in Graph Neural Networks

    Detects echo chamber bias in GNNs, highlighting reliability concerns for graph models. · for ml engineers, graph researchers

    arxivSteadyai35d ago
  3. 3
    16

    Inductive Correlation Clustering with Graph Neural Networks

    Inductive correlation clustering method leveraging GNNs for unsupervised graph grouping. · for ml engineers

    arxivSteadyai47d ago