Preventing Unraveling in Social Networks Gets Harder

The behavior of users in social networks is often observed to be affected by the actions of their friends. Bhawalkar et al. \cite{bhawalkar-icalp} introduced a formal mathematical model for user engagement in social networks where each individual derives a benefit proportional to the number of its friends which are engaged. Given a threshold degree $k$ the equilibrium for this model is a maximal subgraph whose minimum degree is $\geq k$. However the dropping out of individuals with degrees less than $k$ might lead to a cascading effect of iterated withdrawals such that the size of equilibrium subgraph becomes very small. To overcome this some special vertices called "anchors" are introduced: these vertices need not have large degree. Bhawalkar et al. \cite{bhawalkar-icalp} considered the \textsc{Anchored $k$-Core} problem: Given a graph $G$ and integers $b, k$ and $p$ do there exist a set of vertices $B\subseteq H\subseteq V(G)$ such that $|B|\leq b, |H|\geq p$ and every vertex $v\in H\setminus B$ has degree at least $k$ is the induced subgraph $G[H]$. They showed that the problem is NP-hard for $k\geq 2$ and gave some inapproximability and fixed-parameter intractability results. In this paper we give improved hardness results for this problem. In particular we show that the \textsc{Anchored $k$-Core} problem is W[1]-hard parameterized by $p$, even for $k=3$. This improves the result of Bhawalkar et al. \cite{bhawalkar-icalp} (who show W[2]-hardness parameterized by $b$) as our parameter is always bigger since $p\geq b$. Then we answer a question of Bhawalkar et al. \cite{bhawalkar-icalp} by showing that the \textsc{Anchored $k$-Core} problem remains NP-hard on planar graphs for all $k\geq 3$, even if the maximum degree of the graph is $k+2$. Finally we show that the problem is FPT on planar graphs parameterized by $b$ for all $k\geq 7$.Comment: To appear in AAAI 201

Parameterized Complexity of the Anchored k-Core Problem for Directed Graphs

We consider the Directed Anchored k-Core problem, where the task is for a given directed graph G and integers b, k and p, to find an induced subgraph H with at least p vertices (the core) such that all but at most b vertices (the anchors) of H have in-degree at least k. For undirected graphs, this problem was introduced by Bhawalkar, Kleinberg, Lewi, Roughgarden, and Sharma [ICALP 2012]. We undertake a systematic analysis of the computational complexity of Directed Anchored k-Core and show that: - The decision version of the problem is NP-complete for every k>=1 even if the input graph is restricted to be a planar directed acyclic graph of maximum degree at most k+2. - The problem is fixed parameter tractable (FPT) parameterized by the size of the core p for k=1, and W[1]-hard for k>=2. - When the maximum degree of the graph is at most Delta, the problem is FPT parameterized by p+Delta if k>=Delta/2

A fit to the simultaneous broadband spectrum of Cygnus X-1 using the transition disk model

We have used the transition disk model to fit the simultaneous broad band ($2-500$ keV) spectrum of Cygnus X-1 from OSSE and Ginga observations. In this model, the spectrum is produced by saturated Comptonization within the inner region of the accretion disk, where the temperature varies rapidly with radius. In an earlier attempt, we demonstrated the viability of this model by fitting the data from EXOSAT, XMPC balloon and OSSE observations, though these were not made simultaneously. Since the source is known to be variable, however, the results of this fit were not conclusive. In addition, since only once set of observations was used, the good agreement with the data could have been a chance occurrence. Here, we improve considerably upon our earlier analysis by considering four sets of simultaneous observations of Cygnus X-1, using an empirical model to obtain the disk temperature profile. The vertical structure is then obtained using this profile and we show that the analysis is self- consistent. We demonstrate conclusively that the transition disk spectrum is a better fit to the observations than that predicted by the soft photon Comptonization model. Since the temperature profile is obtained by fitting the data, the unknown viscosity mechanism need not be specified. The disk structure can then be used to infer the viscosity parameter $\alpha$, which appears to vary with radius and luminosity. This behavior can be understood if $\alpha$ depends intrinsically on the local parameters such as density, height and temperature. However, due to uncertainties in the radiative transfer, quantitative statements regarding the variation of $\alpha$ cannot yet be made.Comment: 8 figures. uses aasms4.sty, accepted by ApJ (Mar 98

Fourier Neural Operator Networks: A Fast and General Solver for the Photoacoustic Wave Equation

Simulation tools for photoacoustic wave propagation have played a key role in advancing photoacoustic imaging by providing quantitative and qualitative insights into parameters affecting image quality. Classical methods for numerically solving the photoacoustic wave equation relies on a fine discretization of space and can become computationally expensive for large computational grids. In this work, we apply Fourier Neural Operator (FNO) networks as a fast data-driven deep learning method for solving the 2D photoacoustic wave equation in a homogeneous medium. Comparisons between the FNO network and pseudo-spectral time domain approach demonstrated that the FNO network generated comparable simulations with small errors and was several orders of magnitude faster. Moreover, the FNO network was generalizable and can generate simulations not observed in the training data