What does "Random Walk Sampling" mean?

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Random walk sampling is a method used to gather information from graphs or networks. In simple terms, it involves starting at a random point in the graph and then moving from one node to another based on certain rules, like the links between them. This process repeats many times to explore different parts of the graph.

This technique is useful because it allows us to sample large networks without needing to look at every single node or link. It can capture the structure and characteristics of the graph with less effort. The randomness helps ensure that the samples represent the overall network well.

Applications of random walk sampling include studying social networks, finding patterns in data, and improving the performance of algorithms that analyze graphs. By using this method, researchers can gain insights into how different nodes interact and the relationships within the network.