We identify two complementary families of algorithms: iteration of matrix-vector multiplication and iteration of matrix-matrix multiplication. We show all problems can be solved with a unified algorithm with an iteration of matrix multiplications. We present intuitive theory results on cardinality ...
“Can the algorithm complete the task within an acceptable amount of time for a specific set of data derived from a practical application?” As we will see in the next section, there are methods for quantifying the efficiency of an algorithm. For a given problem, different algorithms can be...
Graph Algorithms: Graph databases often include a set of built-in graph algorithms for tasks like finding the shortest path, detecting communities, calculating centrality measures, and more. These algorithms leverage the graph structure to provide insights into the data, making them valuable for variou...
reducing the time and space complexity of existing algorithms. Relaxation Enlarging the space of search is mainly done by relaxation, that can be defined as relaxing constraints. Relaxation is mainly done on queries, but can also be used on documents. When relaxing on queries, one attempts to ...
Sorting and Searching Algorithms Bubble Sort Selection Sort Insertion Sort Merge Sort Quicksort Counting Sort Radix Sort Bucket Sort Heap Sort Shell Sort Linear Search Binary Search Greedy Algorithms Greedy Algorithm Ford-Fulkerson Algorithm Dijkstra's Algorithm Kruskal's Algorithm Prim's Algorithm Huffman...
Graph layout algorithms used in network visualization represent the first and the most widely used tool to unveil the inner structure and the behavior of complex networks. Current network visualization software relies on the force-directed layout (FDL) algorithm, whose high computational complexity makes...
We propose two new algorithms for clustering graphs and networks. The first, called K‑algorithm, is derived directly from the k-means algorithm. It a
We present GRAPE (Graph Representation Learning, Prediction and Evaluation), a software resource for graph processing and embedding that is able to scale with big graphs by using specialized and smart data structures, algorithms, and a fast parallel implementation of random-walk-based methods. ...
Complexity: The time complexity of the two-step sampled group testing algorithms consists of the complexity of finding the optimal M given F m and F , the complexity of the construction of the F -separable test matrix given M and F , and the complexity of the decoding of the test results...
Bandeira, Joan Bruna, A Note on Learning Algorithms for Quadratic Assignment with Graph Neural Networks, 2017 3. Spatial methods for manifolds 3.1 Papers J. Masci, D. Boscaini, M. M. Bronstein, P. Vandergheynst, Geodesic convolutional neural networks on Riemannian manifolds, 3dRR 2015 (...