1,091 research outputs found
Identifying the community structure of the international food-trade multi network
Achieving international food security requires improved understanding of how
international trade networks connect countries around the world through the
import-export flows of food commodities. The properties of food trade networks
are still poorly documented, especially from a multi-network perspective. In
particular, nothing is known about the community structure of food networks,
which is key to understanding how major disruptions or 'shocks' would impact
the global food system. Here we find that the individual layers of this network
have densely connected trading groups, a consistent characteristic over the
period 2001 to 2011. We also fit econometric models to identify social,
economic and geographic factors explaining the probability that any two
countries are co-present in the same community. Our estimates indicate that the
probability of country pairs belonging to the same food trade community depends
more on geopolitical and economic factors -- such as geographical proximity and
trade agreements co-membership -- than on country economic size and/or income.
This is in sharp contrast with what we know about bilateral-trade determinants
and suggests that food country communities behave in ways that can be very
different from their non-food counterparts.Comment: 47 pages, 19 figure
Recent Advances in Graph Partitioning
We survey recent trends in practical algorithms for balanced graph
partitioning together with applications and future research directions
Understanding Coarsening for Embedding Large-Scale Graphs
A significant portion of the data today, e.g, social networks, web
connections, etc., can be modeled by graphs. A proper analysis of graphs with
Machine Learning (ML) algorithms has the potential to yield far-reaching
insights into many areas of research and industry. However, the irregular
structure of graph data constitutes an obstacle for running ML tasks on graphs
such as link prediction, node classification, and anomaly detection. Graph
embedding is a compute-intensive process of representing graphs as a set of
vectors in a d-dimensional space, which in turn makes it amenable to ML tasks.
Many approaches have been proposed in the literature to improve the performance
of graph embedding, e.g., using distributed algorithms, accelerators, and
pre-processing techniques. Graph coarsening, which can be considered a
pre-processing step, is a structural approximation of a given, large graph with
a smaller one. As the literature suggests, the cost of embedding significantly
decreases when coarsening is employed. In this work, we thoroughly analyze the
impact of the coarsening quality on the embedding performance both in terms of
speed and accuracy. Our experiments with a state-of-the-art, fast graph
embedding tool show that there is an interplay between the coarsening decisions
taken and the embedding quality.Comment: 10 pages, 6 figures, submitted to 2020 IEEE International Conference
on Big Dat
Detecting hierarchical and overlapping network communities using locally optimal modularity changes
Agglomerative clustering is a well established strategy for identifying
communities in networks. Communities are successively merged into larger
communities, coarsening a network of actors into a more manageable network of
communities. The order in which merges should occur is not in general clear,
necessitating heuristics for selecting pairs of communities to merge. We
describe a hierarchical clustering algorithm based on a local optimality
property. For each edge in the network, we associate the modularity change for
merging the communities it links. For each community vertex, we call the
preferred edge that edge for which the modularity change is maximal. When an
edge is preferred by both vertices that it links, it appears to be the optimal
choice from the local viewpoint. We use the locally optimal edges to define the
algorithm: simultaneously merge all pairs of communities that are connected by
locally optimal edges that would increase the modularity, redetermining the
locally optimal edges after each step and continuing so long as the modularity
can be further increased. We apply the algorithm to model and empirical
networks, demonstrating that it can efficiently produce high-quality community
solutions. We relate the performance and implementation details to the
structure of the resulting community hierarchies. We additionally consider a
complementary local clustering algorithm, describing how to identify
overlapping communities based on the local optimality condition.Comment: 10 pages; 4 tables, 3 figure
Hypergraph Partitioning With Embeddings
Problems in scientific computing, such as distributing large sparse matrix
operations, have analogous formulations as hypergraph partitioning problems. A
hypergraph is a generalization of a traditional graph wherein "hyperedges" may
connect any number of nodes. As a result, hypergraph partitioning is an NP-Hard
problem to both solve or approximate. State-of-the-art algorithms that solve
this problem follow the multilevel paradigm, which begins by iteratively
"coarsening" the input hypergraph to smaller problem instances that share key
structural features. Once identifying an approximate problem that is small
enough to be solved directly, that solution can be interpolated and refined to
the original problem. While this strategy represents an excellent trade off
between quality and running time, it is sensitive to coarsening strategy. In
this work we propose using graph embeddings of the initial hypergraph in order
to ensure that coarsened problem instances retrain key structural features. Our
approach prioritizes coarsening within self-similar regions within the input
graph, and leads to significantly improved solution quality across a range of
considered hypergraphs. Reproducibility: All source code, plots and
experimental data are available at https://sybrandt.com/2019/partition
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