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Large-Scale Community Detection on YouTube

Large-Scale Community Detection on YouTube 2025

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The document presents a multi-stage community detection algorithm designed for large-scale graphs, specifically applied to YouTube videos. It addresses the challenges of traditional clustering methods by combining pre-processing, local clustering, and post-processing stages to generate coherent video clusters based on content similarity. The proposed method optimizes various metrics such as coverage, coherence, and connectivity, allowing for efficient exploration and discovery of topics within the vast collection of YouTube videos. The paper highlights the effectiveness of local algorithms in a global context and introduces a novel approach to cluster labeling using extracted entities.

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How does Louvain community detection work?

The Louvain method works by repeating two phases. In phase one, nodes are sorted into communities based on how the modularity of the graph changes when a node moves communities. In phase two, the graph is reinterpreted so that communities are seen as individual nodes.

What is the difference between clustering and community detection?

Often clustering and community detection are used interchangeably in the literature. Clustering mostly focuses on a single modality, e.g., using node attributes to group network objects, whereas community detection focuses on network structure as a function of connectivity involving social interaction.

What is the purpose of community detection?

Community detection aims at discovering the structure, behavior, dynamics, and organization of a complex network by finding cohesive groups where nodes (entities) are, in some sense, more similar within the group and groups are in some fashion separated from the other groups.

What are the approaches to community detection?

Six community detection methods are discussed: Walktrap, Edge-Betweenness, Infomap, Louvain, Label Propagation, and Spinglass. The Question-Alignment approach is described and demonstrated using real-world data collected in 2013.

How to choose a community detection algorithm?

Consider the size and complexity of the network The size and complexity of your network play a significant role in determining which algorithm to use. For smaller networks, algorithms like Girvan-Newman, which have higher computational complexity, can be effective.

What is the difference between community search and community detection?

(e) Community detection is finding all communities over the whole network. Each community is colored dif- ferently. (f) Community search is to find communities containing the given query nodes in the graph. It is a query-dependent community discovery.

Is community detection NP hard?

The classification of community detection as an NP-hard problem underscores the need for developing scalable and efficient algorithms to handle real-world networks.

What is the meaning of community detection?

Community detection is the process of grouping nodes in a network based on their internal connections, with the goal of finding clusters that are more densely connected internally than externally.

Large-Scale Community Detection on YouTube 2025

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How does Louvain community detection work?

What is the difference between clustering and community detection?

What is the purpose of community detection?

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What is the difference between community search and community detection?

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Large-Scale Community Detection on YouTube 2025

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Got questions?

How does Louvain community detection work?

What is the difference between clustering and community detection?

What is the purpose of community detection?

What are the approaches to community detection?

How to choose a community detection algorithm?

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What is the difference between community search and community detection?

Is community detection NP hard?

What is the meaning of community detection?

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