Neo4j data science algorithms3

Question 1

Graph Isomorphism

Answer 1

A concept in graph theory where two graphs are considered isomorphic if there is a one-to-one correspondence between their nodes and edges that preserves adjacency. For example, graph isomorphism can be used to detect structural similarities in chemical compounds.

Question 2

Graph Partitioning

Answer 2

The process of dividing a graph into smaller, disjoint subgraphs while minimizing the number of edges between them. For example, graph partitioning is used in parallel computing to distribute workloads efficiently.

Question 3

Walktrap Algorithm

Answer 3

A community detection algorithm based on random walks, which groups nodes into communities by considering the probability of random walks staying within the same community. For example, Walktrap can identify tightly connected groups in social networks.

Question 4

Shortest Path Algorithms

Answer 4

Algorithms that find the shortest path between nodes in a graph, such as Dijkstra’s and Bellman-Ford. For example, these algorithms can be used for routing in transportation networks.

Question 5

Graph Database Sharding

Answer 5

A technique used to distribute a graph database across multiple servers or clusters to improve scalability and performance. For example, sharding can be used to handle large-scale graph data across distributed systems.

Question 6

Triadic Closure

Answer 6

The tendency for two nodes with a common neighbor to form a direct connection, a concept used in social network analysis. For example, triadic closure is used to predict the likelihood of friendships forming in social networks.

Question 7

Motif Detection

Answer 7

The process of identifying recurring subgraph patterns in a graph, which can reveal underlying structural or functional properties. For example, motif detection is used in bioinformatics to identify common interaction patterns in protein networks.

Question 8

GraphQL Cypher

Answer 8

A Neo4j extension that combines GraphQL with the Cypher query language to allow flexible and powerful querying of graph data. For example, GraphQL Cypher enables complex queries on graph databases with a simple API.

Question 9

Bipartite Graph

Answer 9

A type of graph where nodes can be divided into two disjoint sets such that no two nodes within the same set are adjacent. For example, a bipartite graph can model relationships between users and the items they purchase.

Question 10

Neo4j APOC (Awesome Procedures on Cypher)

Answer 10

A library of utilities and functions that extend the capabilities of Cypher, Neo4j’s query language. For example, APOC provides functions for data integration, graph algorithms, and data transformation.

Question 11

Personalized PageRank

Answer 11

A variation of the PageRank algorithm that computes the importance of nodes with respect to a personalized set of starting nodes. For example, Personalized PageRank can be used in recommendation systems to prioritize items similar to those a user has interacted with.

Question 12

Betweenness Centrality

Answer 12

A measure of a node’s importance based on the number of shortest paths that pass through it, used to identify critical nodes in a network. For example, nodes with high betweenness centrality are often key connectors in communication networks.

Question 13

Markov Clustering Algorithm (MCL)

Answer 13

A scalable graph clustering algorithm based on flow simulations, which identifies clusters by simulating random walks. For example, MCL is used in bioinformatics for clustering proteins based on their interaction networks.

Question 14

K-Core Decomposition

Answer 14

A method for decomposing a graph into subgraphs where each node has at least k connections, used to identify dense regions. For example, k-core decomposition can be used to find influential groups in social networks.

Question 15

Dynamic Graphs

Answer 15

Graphs that change over time, where nodes and edges can be added or removed, requiring specialized algorithms for analysis. For example, dynamic graph analysis is used to study evolving social networks or transaction networks.

Question 16

GraphBLAS

Answer 16

A specification for implementing graph algorithms using linear algebra operations, enabling efficient computations on large graphs. For example, GraphBLAS can be used to optimize graph analytics in high-performance computing environments.

Question 17

Graph Neural Network Explainers

Answer 17

Techniques used to interpret the predictions of Graph Neural Networks by identifying important nodes and edges. For example, explainers can help understand why a GNN classified a node in a certain way.

Question 18

Homophily

Answer 18

The tendency for nodes in a graph to connect with similar nodes, a common phenomenon in social networks. For example, homophily explains why people with similar interests are more likely to become friends.

Question 19

Graph Convolutional Autoencoders

Answer 19

Models that learn graph representations by encoding node features and reconstructing graph structures, used for tasks like anomaly detection. For example, these autoencoders can identify unusual patterns in network traffic data.

Question 20

Neo4j Fabric

Answer 20

A feature in Neo4j that enables the querying of multiple databases as if they were a single, federated database. For example, Neo4j Fabric allows seamless integration and querying across distributed graph datasets.

Question 21

Gibbs Sampling for Graphs

Answer 21

A Markov Chain Monte Carlo algorithm used to sample from the probability distribution of node labels, often used in Bayesian networks. For example, Gibbs sampling can be used to infer missing labels in a partially labeled graph.