🧠 Multimodal AI Network Graph Analysis

Seed Node: benchingtrains | Task MUID: benchingtrains_1_hashtagTop_9_ade5af42

Mining Type: hashtagTopPosts: 82Hashtags: 18Users: 58Inferences: 80

Network will include:

Standard

Text_AI_Dict

Text_AI_OOV

Image_AI

Select the data used to feed the graph. Standard [hashtags, posts, users]. Text_AI: Hashtags classified by [Northamerica city list, Graffiti terms, Railroad terms]. Image_AI: Post classified by graffiti types [tag, wildstyle, 3D, monikers, bomba (throwup)]

Node Minimum Degree (0)Minimum entrance degree of node, smaller will be deleted from graph. Default: 0

Should we clean not related text_ai_entities to term "graffiti", numbers only and non latin chars?

true

false

Gravity (1)

Should the node's sizes be taken into account?

true

false

Use Barnes-Hut approximation O(n·log n)?

true

false

Barnes-Hut approximation theta (0.5)

outboundAttractionDistribution

true

false

Use Noack's LinLog model?

true

false

scalingRatio (1)

slowDown (1)

strongGravityMode

true

false

nodeFixedSize

true

false

initialLayout

random

circlepack

autoGravityScale

auto

manual

nodeReducerDepth *

selectedNeighbors (1-hop)

selectedNeighborsNeighbors (2-hop)

Building AI multi-layer graph network...

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🧠 Multimodal AI Network Graph Analysis

Multi-Layer AI Network Graph Analysis

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