Changes

By interpreting the tree as a graph, we can utilize an adjacency matrix to encode the tree. The elements of the matrix represent whether their corresponding vertices are adjacent in the graphical representation. In its simplest form, for a set of V number of vertices, the matrix would be a square matrix of dimensions |V| x |V|. The diagonal elements of such a matrix would all be zero. This approach has an algorithmic efficiency of O(n^2).

==== Edges to Vertices Matrix ====

For any given tree, we have n-1 (I'm assuming n = number of nodes) edges. For every edge, we can record the two ending vertices. This will result in a matrix of dimensions (n-1) x 2. This matrix approach has an algorithmic efficiency of O(n).

==== node2vec ====

==Literature==

=== HTML Tree Structure Analysis ===

* [https://arxiv.org/abs/1207.0246 Web Data Extraction, Applications and Techniques: A Survey (Ferrara et al.)]

: This survey of various web data extraction methods includes a section on tree-based analysis in Chapter 2: Techniques.

=== DFS Encoding ===

* [https://arxiv.org/abs/1607.00653 node2vec: Scalable Feature Learning for Networks (Grover, Leskovec)]

: node2vec was mentioned briefly in the above Hamilton et al. paper. node2vec is a scalable encoding algorithm that focuses on preserving network neighborhoods of nodes. The definition of a neighborhood can manipulated depending on the application context. In section 3.1 (Classic Search Strategies), node2vec is compared to BFS and DFS approaches.

* [http://cs229.stanford.edu/proj2013/YaoZuo-AMachineLearningApproachToWebpageContentExtraction.pdf A Machine Learning Approach to Webpage Content Extraction (Yao, Zuo)]

: This article describes methods to simplify the content of a noisy HTML page, specifically through using machine learning to predict whether a block is content or non-content. This allows the classifier to remove boilerplate information.