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UID:submissions.pasc-conference.org_PASC24_sess124@linklings.com
SUMMARY:MS4F - High Performance Graph Analytics
DESCRIPTION:Minisymposium\n\nEstimating the structure, partitioning, and a
 nalyzing graphs, are all critical tasks in a plethora of applications. Pro
 blems in domains such as image processing, social network analysis, and cl
 assification via neural networks, are often formulated as being graph-base
 d. Simultaneously, graph analytic methods are traditionally an important s
 ubtask that enables the parallelization or the complexity reduction of the
  entire algorithmic workflow. This minisymposium samples recent advances i
 n methods intended for graphs emerging from large-scale data, with a focus
  on performant, efficient, and scalable algorithms.\n\nAlgebraic Programmi
 ng for Graph & Machine Learning\n\nAlgebraic Programming, or ALP for short
 , allows for programming with explicit algebraic structures. Such structur
 es range from the simplistic such as associative binary operators, to rich
 er constructs such as semirings. This algebraic knowledge, given to ALP by
  the programmer, percolates through the...\n\n\nAlbert-Jan Yzelman, Ariste
 idis Mastoras, and Alberto Scolari (Huawei)\n---------------------\nParall
 el Algorithms for Dynamic Graph Clustering\n\nWe consider the problem of i
 ncremental graph clustering where the graph to be clustered is given as a 
 sequence of disjoint subsets of the edge set. The problem appears when dea
 ling with graphs that are created over time, such as online social network
 s where new users appear continuously, or protein ...\n\n\nJohannes Langgu
 th (Simula Research Laboratory, University of Bergen)\n-------------------
 --\n3S in Distributed Graph Neural Networks: Sparse Communication, Samplin
 g, and Scalability\n\nThis talk will focus on distributed-memory parallel 
 algorithms for graph neural network (GNN) training. We will first focus on
  utilizing sparse matrix primitives to parallelize mini-batch training bas
 ed on node-wise and layer-wise sampling. Then, we will illustrate techniqu
 es that are based on spars...\n\n\nAydin Buluc (Lawrence Berkeley National
  Laboratory, UC Berkeley); Alok Tripathy (UC Berkeley); and Katherine Yeli
 ck (UC Berkeley, Lawrence Berkeley National Laboratory)\n-----------------
 ----\nFaster Local Motif Clustering via Maximum Flows\n\nLocal clustering 
 aims to identify a cluster within a given graph that includes a designated
  seed node or a significant portion of a group of seed nodes. This cluster
  should be well-characterized, i.e., in the context of motifs, such as edg
 es or triangles, it should have a high number of internal mot...\n\n\nAdil
  Chhabra, Marcelo Fonseca Faraj, and Christian Schulz (University of Heide
 lberg)\n\nDomain: Computational Methods and Applied Mathematics\n\nSession
  Chairs: Dimosthenis Pasadakis (Università della Svizzera italiana) and Ol
 af Schenk (Università della Svizzera italiana, ETH Zurich)
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