16:20  16:40
Novel Dense Subgraph Discovery Primitives: Risk Aversion and Exclusion Queries (192)
Charalampos E. Tsourakakis (Boston University), Tianyi Chen (Boston University), Naonori Kakimura (Keio University), Jakub Pachocki (OpenAI)
In the densest subgraph problem, given an undirected graph G(V,E,w) with nonnegative edge weights we are asked to find a set of nodes S⊆ V that maximizes the degree density w(S)/S, where w(S) is the sum of the weights of the edges in the graph induced by S. This problem is solvable in polynomial time, and in general is well studied. But what happens when the edge weights are negative? Is the problem still solvable in polynomial time? Also, why should we care about the densest subgraph problem in the presence of negative weights? In this work we answer the aforementioned questions. Specifically, we provide two novel graph mining primitives that are applicable to a wide variety of applications. Our primitives can be used to answer questions such as "how can we find a dense subgraph in Twitter with lots of replies and mentions but no follows?", "how do we extract a dense subgraph with high expected reward and low risk from an uncertain graph"? We formulate both problems mathematically as special instances of dense subgraph discovery in graphs with negative weights. We study the hardness of the problem, and we prove that the problem in general is NPhard, but we also provide sufficient conditions under which it is polytime solvable. We design an efficient approximation algorithm that works best in the presence of small negative weights, and an effective heuristic for the more general case. Finally, we perform experiments on various realworld datasets that verify the value of the proposed primitives, and the effectiveness of our proposed algorithms.
Reproducible Research

16:40  17:00
Graph Signal Processing for Directed Graphs based on the Hermitian Laplacian (499)
Satoshi Furutani (NTT Secure Platform Laboratories, Tokyo), Toshiki Shibahara (NTT Secure Platform Laboratories, Tokyo), Mitsuaki Akiyama (NTT Secure Platform Laboratories, Tokyo), Kunio Hato (NTT Secure Platform Laboratories, Tokyo), Masaki Aida (Tokyo Metropolitan University)
Graph signal processing is a useful tool for representing, analyzing, and processing the signal lying on a graph, and has attracted attention in several fields including data mining and machine learning.A key to construct the graph signal processing is the graph Fourier transform, which is defined by using eigenvectors of the graph Laplacian of an undirected graph.The orthonormality of eigenvectors gives the graph Fourier transform algebraically desirable properties, and thus the graph signal processing for undirected graphs has been well developed.However, since eigenvectors of the graph Laplacian of a directed graph are generally not orthonormal, it is difficult to simply extend the graph signal processing to directed graphs.In this paper, we present a general framework for extending the graph signal processing to directed graphs.To this end, we introduce the Hermitian Laplacian which is a complex matrix obtained from an extension of the graph Laplacian.The Hermitian Laplacian is defined so as to preserve the edge directionality and Hermitian property and enables the graph signal processing to be straightforwardly extended to directed graphs.Furthermore, the Hermitian Laplacian guarantees some desirable properties, such as nonnegative real eigenvalues and the unitarity of the Fourier transform.Finally, experimental results for representation learning and signal denoising of/on directed graphs show the effectiveness of our framework.

17:00  17:20
Learning AlignedSpatial Graph Convolutional Networks for Graph Classification (542)
Lu Bai (Central University of Finance and Economics, Beijing, China), Yuhang Jiao (Central University of Finance and Economics, Beijing, China), Lixin Cui (Central University of Finance and Economics, Beijing, China), Edwin R. Hancock (Central University of Finance and Economics, Beijing, China)
In this paper, we develop a novel AlignedSpatial Graph Convolutional Network (ASGCN) model to learn effective features for graph classification. Our idea is to transform arbitrarysized graphs into fixedsized aligned grid structures, and define a new spatial graph convolution operation associated with the grid structures. We show that the proposed ASGCN model not only reduces the problems of information loss and imprecise information representation arising in existing spatiallybased Graph Convolutional Network (GCN) models, but also bridges the theoretical gap between traditional Convolutional Neural Network (CNN) models and spatiallybased GCN models. Moreover, the proposed ASGCN model can adaptively discriminate the importance between specified vertices during the process of spatial graph convolution, explaining the effectiveness of the proposed model. Experiments on standard graph datasets demonstrate the effectiveness of the proposed model.

17:40  18:00
node2bits: Compact Time and Attributeaware Node Representations for User Stitching (597)
Di Jin (University of Michigan), Mark Heimann (University of Michigan), Ryan A. Rossi (Adobe Research), Danai Koutra (University of Michigan)
Identity stitching, the task of identifying and matching various online references (e.g., sessions over different devices and timespans) to the same user in realworld web services, is crucial for personalization and recommendations. However, traditional user stitching approaches, such as grouping or blocking, require quadratic pairwise comparisons between a massive number of user activities, thus posing both computational and storage challenges. Recent works, which are often applicationspecific, heuristically seek to reduce the amount of comparisons, but they suffer from low precision and recall. To solve the problem in an applicationindependent way, we take a heterogeneous networkbased approach in which users (nodes) interact with content (e.g., sessions, websites), and may have attributes (e.g., location). We propose node2bits, an efficient framework that represents multidimensional features of node contexts with binary hashcodes.node2bits leverages featurebased temporal walks to encapsulate short and longterm interactions between nodes in heterogeneous web networks, and adopts SimHash to obtain compact, binary representations and avoid the quadratic complexity for similarity search.Extensive experiments on largescale real networks show that node2bits outperforms traditional techniques and existing works that generate realvalued embeddings by up to 5.16
Reproducible Research

17:20  17:40
A Soft Affiliation Graph Model for Scalable Overlapping Community Detection (618)
Nishma Laitonjam (Insight Centre for Data Analytics, University College Dublin), Wěipéng Huáng (Insight Centre for Data Analytics, University College Dublin), Neil J. Hurley (Insight Centre for Data Analytics, University College Dublin)
We propose an overlapping community model based on the Affiliation Graph Model (AGM), that exhibits the pluralistic homophily property that the probability of a link between nodes increases with increasing number of shared communities. We take inspiration from the Mixed Membership Stochastic Blockmodel (MMSB), in proposing an edgewise community affiliation. This allows decoupling of community affiliations between nodes, opening the way to scalable inference. We show that our model corresponds to an AGM with soft community affiliations and develop a scalable algorithm based on a Stochastic Gradient Riemannian Langevin Dynamics(SGRLD) sampler. Empirical results show that the model can scale to network sizes that are beyond the capabilities of MCMC samplers of the standard AGM. We achieve comparable performance in terms of accuracy and runtime efficiency to scalable MMSB samplers.
Reproducible Research
