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Planting trees in random graphs (and finding them back)

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Presentation on theme: "Planting trees in random graphs (and finding them back)"— Presentation transcript:

1 Planting trees in random graphs (and finding them back)
Laurent Massoulié Joint work with Ludovic Stephan & Don Towsley

2 Inference problems with planted structure
Classical examples community detection, planted clique detection, planted dense subgraph detection Typical Objectives -Detection (planted structure present or not) -Recovery (estimation of planted structure) Recurring phenomena  3 regimes, or phases: -Information-Theoretic impossibility -Computational hardness though IT-feasible -Computationally easy  Boundary phases the same for both detection and reconstruction

3 ‘Network Security’ scenario
Attackers may be “plotting” or not Plotting  communications among them Goals: -Infer from observed communication graph whether attack under way (detection) -If attack detected, identify attackers (reconstruction)

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7 Model Erdös-Rényi graph Under attack: augmented by tree on nodes chosen uniformly at random Focus on simplest tree i.e. path

8 Outline Phase diagram for paths Other trees

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11 Sparse phase

12 First moment method: no K-path in G under  poly-time detection and reconstruction

13 The argument for undetectability
Likelihood ratio between distributions without and with attack,

14 The argument for undetectability
Likelihood ratio between distributions without and with attack,

15 Bounding Markov chain

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17 The argument for detectability,
Too many edges under Counts of small connected components enable consistent estimation of Arguments hold for any connected K-graph, not just lines and trees

18 The argument for reconstruction impossibility
Optimal overlap between estimated and planted path: Maximum a Posteriori, i.e. nodes on largest number of K-paths

19 The argument for reconstruction impossibility
“Lures”: can construct confounding segment s.t.

20 More generally: construct T symmetric paths

21 Other trees: stars Stars hard to hide! Threshold at
Detection and reconstruction easy: inspection of degrees

22 D-trees

23 Conclusions Phase diagram for line detection & reconstruction -No computationally hard phase -Reconstruction impossible, by presence of too many copies of planted structure Planted subgraphs beyond cliques & dense subgraphs: -Phase diagram for D-regular trees? More general planted graphs? -What triggers hard phases?

24 Thanks!


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