# Abstracts¶

## Graphs and complex networks across domains¶

Graphs arise in many fields. Progress in understanding graphs and developing new graph algorithms in a number of diverse fields is hindered by the fact that researchers who use them typically don’t have the opportunity to communicate with others who work on similar problems in different domains. In this talk, I will (a) briefly define what we mean by a graph or complex network and (b) outline the purpose and goals for this workshop.

## Understanding the ecology and evolution of communities through networks, part I¶

**Lauren Ponisio**(

`slides`

)Prior to the use of networks to model community-level interactions between species, ecology and evolution often focused on pair-wise interactions, which vastly limited the scope of inference. Network theory has enabled ecologists to uncover common topologies across different types of interactions and overturn misconceptions about the ubiquity of specialization in interactions. In addition, models of network stability has enabled us to make predictions about the robustness of communities to species loss and perturbations. The next step to further our understanding of the dynamics of ecological communities include methods to 1) compare networks beyond collapsing their topology into quantitative metrics, 2) combine different types of networks (i.e, pollination, seed dispersal, predation), 3) incorporate metadata into interaction strength and node roles. In addition, ecological data still relies on tedious data collection by humans—innovations in metabarcoding and image processing will also propel our understanding of ecological interactions forward.

## Understanding the ecology and evolution of communities through networks, part II¶

**Marília Palumbo Gaiarsa**(

`slides`

)Prior to the use of networks to model community-level interactions between species, ecology and evolution often focused on pair-wise interactions, which vastly limited the scope of inference. Network theory has enabled ecologists to uncover common topologies across different types of interactions and overturn misconceptions about the ubiquity of specialization in interactions. In addition, models of network stability has enabled us to make predictions about the robustness of communities to species loss and perturbations. The next step to further our understanding of the dynamics of ecological communities include methods to 1) compare networks beyond collapsing their topology into quantitative metrics, 2) combine different types of networks (i.e, pollination, seed dispersal, predation), 3) incorporate metadata into interaction strength and node roles. In addition, ecological data still relies on tedious data collection by humans—innovations in metabarcoding and image processing will also propel our understanding of ecological interactions forward.

## A history of spectral graph theory and its applications, part I¶

Spectral graph theory gives an expression of the combinatorial properties of a graph using the eigenvalues and eigenvectors of matrices associated with the graph. These ideas were first introduced in the late 80s in order to prove Cheeger’s inequality for finding a sparse cut. The utility of spectral graph theory eventually stretched to Laplacian systems for solving linear equations. Implementing graph sparsification gives us the ability to do this quickly. In this talk we will describe the introduction of the Laplacian matrix and its use in these areas.

## A history of spectral graph theory and its applications, part II¶

Spectral graph theory started in the 80s, when Cheeger’s inequality was used as a means for constructing sparse and balanced cuts in a graph. In the 2000s, our field moved on from studying specific eigenvalues to studying the whole spectrum of the Laplacian matrix with fast Laplacian solvers. To obtain fast Laplacian solvers, we needed to sparsify graphs, for which we exploited concentration phenomena of random matrices. In the 2010s, improvements to these tools led to improvements on a wide variety of problems, like maximum flow, travelling salesman (both symmetric and asymmetric), and random spanning tree generation. In this talk, we briefly survey this chain of events and suggest some future directions.

## Exploring network structure, dynamics, and function using NetworkX¶

**Aric Hagberg**

NetworkX is a software tool for network science. I’ll tell the previously untold story of how the software project started at Los Alamos and describe the original design goals. The software scope was driven by research applications such as disease spread, cybersecurity, and measuring scholarly impact. I’ll describe these applications and the algorithms and analysis that were developed to support them.

## Graph abstractions in computational genomics¶

Relationships among entities in genomics, such as proteins, DNA sequences, or genetics markers, are often represented as graphs. Graph abstraction helped solve many important computational genomics problems from building linkage maps to genome assembly. In this talk, I will give a short overview on the characteristics of the graphs that arise in key computational genomics tasks as well as the fundamental algorithmic questions that are addressed using those graphs.

## Challenges for graph theory in human neuroscience¶

Graph theory has been applied widely to studies of the human brain, advancing understanding of cognition and neurological diseases. However, the application of graph theory to human neuroscience faces many challenges. We will briefly overview examples of the motivation for and insights enabled by modeling the brain as a graph. We will then explore challenges for graph theory in human neuroscience, including challenges with defining the elements of the graphs, comparing graphs, and calculating and computing informative graph properties. Examples will be drawn from my work applying graph theory to neuroimaging (MRI and PET) to study human aging and Alzheimer’s disease.

## Variational Perspective on Local Graph Clustering¶

**Kimon Fountoulakis**(

`slides`

)Local spectral methods such as the Approximate Personalized PageRank (APPR) algorithm have proven to be a powerful tool for the analysis of large data graphs. They are defined operationally, and while they come with strong theory, there is no a priori notion of objective function/optimality condition that characterizes the steps taken by them. Here, we derive a novel variational formulation which makes explicit the actual optimization problem solved by the APPR algorithm. In doing so, we draw connections between APPR and a popular iterative shrinkage-thresholding algorithm (ISTA). This viewpoint between APPR and ISTA builds a bridge across two seemingly disjoint fields of graph processing and numerical optimization, and it allows one to leverage well-studied, numerically robust, and efficient optimization algorithms for processing today’s large graphs.

## Sequence Assembly Graphs and their Construction¶

The advent of shotgun sequencing of DNA and RNA created the need for more efficient methods of simplifying massive and redundant string data. Assembly graphs have become a core abstraction for detecting overlaps between sequenced fragments, interrogating complete sequencing experiments from one or many individuals or even species, and succinctly encoding this information in a principled way. In this talk, I will cover some of the current approaches for constructing and using assembly graphs, and discuss efforts to move assembly graph construction into a streaming paradigm.

## How to Solve Problems on Graphs Using Linear Equations, and How to Solve Linear Equations Using Graphs¶

Graphs give us a simple model of a network. Based on this model, we can ask many interesting questions. For example, we can analyze social networks using clustering and regression. In transportation networks, we want to understand and plan flows of traffic, goods, or data. Answering our questions often boils down to solving an optimization problem on a graph. Second order methods are a powerful tool in optimization, but they require solving linear equations, which can be prohibitively expensive. But when the optimization problem comes from a graph, this adds structure to the linear equations. We can leverage this structure to solve the equations quickly, making second order methods tractable. This insight has been one of the drivers of a major line of research in graph algorithms, known as the Laplacian Paradigm. In this talk, we will see how graph-structured optimization problems give rise to nice linear equations. We will also see how thinking about these linear equations in terms of graphs will let us develop very efficient algorithms for solving them. Finally, we will explore ideas that have recently played a role in making solvers for these linear equations more practical.

## Linear Regression with Graph Constraints¶

Linear regression is one of the core tools in data analysis. Over the past decade, we have seen significant progress by incorporating prior knowledge such as sparsity, low rank, or group structures that allow us to achieve higher accuracy and more interpretable solutions. However, the resulting estimators also become more challenging algorithmic problems which can be an obstacle to adoption in practice. In this talk, I will give an overview of linear regression with graph constraints that arise in settings such as biological network analysis. On the statistical side, we will see that graph constraints can offer significantly smaller sample complexity. On the computational side, I will present an algorithm that incorporates graph structure into linear regression with little overhead. The algorithm runs in nearly-linear time, i.e., fast enough so that it is applicable to large graphs.