# A Methodology for Knowledge Discovery in Labeled and Heterogeneous Graphs
## 2 Related Work
### Tools and Algorithms for Graph Analysis  
In addition to proposal methodologies and frameworks, relevant works focus on graph mining algorithms with different aims, such as summarizing and visualizing graphs, performing aggregation operations on the data, searching for substructures in the graph, automatic construction of graphs, and developing proposals to optimize operations performed on the graphs. Some works in the literature focus on analyzing subgraphs and transforming the graph topology. [PERSON] [29] proposed a parallel frequent subgraph mining algorithm in a single large graph using Spark. The proposal employs a heuristic search strategy, load balancing, research pruning, and top-down pruning in the support. [PERSON] also proposed a two-phase framework. In the first phase, the parallel subgraph extension uses a strategy that generates all subgraphs in parallel. In the second phase, he uses a similar support evaluation method for finding subgraph isomorphisms. [PERSON] [30] proposed a framework for the weighted meta-graph-based classification of heterogeneous information networks. The core is an algorithm that iterative classifies objects in heterogeneous information networks to capture the information hidden in the semantics and structure of the graph. [PERSON] et al. [31] proposed a method for extracting frequent subgraphs while maintaining semantic information and considering scalability in large-scale graphs. The generated semantic information includes frequency counts for tasks such as rating prediction or recommendation. [PERSON] et al. proposed Vigor [18], an interactive tool for graph exploration that includes both bottom-up and top-down sensemaking for analysts, facilitating their review of subgraphs through a summarization process. This proposal provides valuable contributions to the field of graph exploration. However, there is an opportunity to explore further the business requirements. Additionally, [PERSON] and [PERSON]'s work [32] focused on improving graph visualization through motif simplification, which replaces common patterns of nodes and links with compact and meaningful glyphs, further enhancing graph exploration. [PERSON] and [PERSON] investigated the problem of aggregation on different types of nodes and relations by proposing a function bases on graph entropy to measure the similarities of nodes. They also proved that the aggregation problem based on the functions is NP-hard and proposes a heuristic algorithm to perform aggregation, including informational and structural aspects. Despite the study's limited scope of the algorithm proposal, their work is significant in addressing the fundamental operation of performing aggregations on data stored in graph nodes. Searching, processing, and visualizing subgraphs can be a task that requires many computational resources. For this reason, authors such as [PERSON] et al. [19] have developed proposals on enhancing subgraph accessibility. They propose a two-level caching strategy that caches subgraphs that are likely to be accessed depending on the usage pattern of subgraphs. Their proposal prevents the caching of low-usage subgraphs and frequent subgraph replacement in the memory. One of their proposal's main applications is enabling the processing and analyzing of large graph queries in computing environments with small memory. In the last decade, various proposals have emerged that focus on different phases of knowledge discovery in graph databases. These proposals encompass areas such as business understanding [16], data modeling [17], graph mining [18; 19; 20], graph visualization [21; 22], and evaluation [23; 24]. The methodologies, frameworks, tools, and algorithms mentioned in Sections 2.1 and 2.2 have demonstrated their value, but there is still a need for a comprehensive methodology that incorporates existing advancements and algorithms. Such a methodology should provide a detailed and specific guide for analyzing related data using graphs, addressing the diverse tasks involved in knowledge discovery.