Random walk
A random walk is a stochastic process that repeatedly transitions from its current state through randomly selected increments or neighboring states.
Graph Neural Networks in Action
A random walk is a stochastic traversal of a graph that repeatedly moves from a node to one of its connected neighbors according to transition probabilities.
- How it is used
- Uses walks to sample graph neighborhoods for Node2Vec embeddings that capture node context and similarity.
- How deep it goes
- Explains encounter probability and DeepWalk versus Node2Vec walk choices without deriving transition probabilities.
- Examples
- Illustrates walks with a two-hop encounter calculation and embeddings of frequently co-purchased political books.
- What you can do after
- Apply it via the Node2Vec Python library to generate embeddings for visualization or machine-learning features.
Read how Graph Neural Networks in Action explains random walk →
Investing for Programmers
A random walk is a stochastic process whose successive movements are determined by random variation rather than a predictable directional pattern.
- How it is used
- Frames stock-price prediction as uncertain analysis rather than a dependable search for deterministic patterns.
- How deep it goes
- Explains randomness through market psychology and interconnected markets, but stops short of formal stochastic-process mathematics.
- Examples
- Uses Mr. Market, tulip mania, global market spillovers, and historical stock performance as concrete illustrations.
- What you can do after
- Evaluate/understand why historical price patterns may provide limited assistance for predicting chaotic markets.
Graph Algorithms for Data Science
A random walk is a stochastic graph traversal that repeatedly moves from the current node to a neighboring node according to transition probabilities.
- How it is used
- Generates graph-based training sentences for node2vec and downstream node embedding models.
- How deep it goes
- Explains walk length, first- and second-order transitions, and weighted probabilities without developing advanced probability or implementation details.
- Examples
- Uses a traversal from A through C, B, and F to H, plus unweighted and weighted neighbor transitions.
- What you can do after
- Apply it via node2vec to generate sentence-like walks for training node embeddings.
Read how Graph Algorithms for Data Science explains random walk →
Related concepts
- Node2Vecused by · Node2Vec is a graph representation-learning algorithm that learns node embeddings from biased random walks and their context relationships.
- corpusproduces · A corpus is an organized collection of documents, texts, or other language data assembled for processing, retrieval, analysis, training, or evaluation.
- encoder-decoder architectureimplements · An encoder-decoder architecture is a model design in which an encoder transforms input into an intermediate representation and a decoder uses that representation to produce output.
- second-order biased random walkincludes · A second-order biased random walk chooses its next node using both the current node and the node visited immediately before it.
These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.