86 research outputs found
Detecting Strong Ties Using Network Motifs
Detecting strong ties among users in social and information networks is a
fundamental operation that can improve performance on a multitude of
personalization and ranking tasks. Strong-tie edges are often readily obtained
from the social network as users often participate in multiple overlapping
networks via features such as following and messaging. These networks may vary
greatly in size, density and the information they carry. This setting leads to
a natural strong tie detection task: given a small set of labeled strong tie
edges, how well can one detect unlabeled strong ties in the remainder of the
network?
This task becomes particularly daunting for the Twitter network due to scant
availability of pairwise relationship attribute data, and sparsity of strong
tie networks such as phone contacts. Given these challenges, a natural approach
is to instead use structural network features for the task, produced by {\em
combining} the strong and "weak" edges. In this work, we demonstrate via
experiments on Twitter data that using only such structural network features is
sufficient for detecting strong ties with high precision. These structural
network features are obtained from the presence and frequency of small network
motifs on combined strong and weak ties. We observe that using motifs larger
than triads alleviate sparsity problems that arise for smaller motifs, both due
to increased combinatorial possibilities as well as benefiting strongly from
searching beyond the ego network. Empirically, we observe that not all motifs
are equally useful, and need to be carefully constructed from the combined
edges in order to be effective for strong tie detection. Finally, we reinforce
our experimental findings with providing theoretical justification that
suggests why incorporating these larger sized motifs as features could lead to
increased performance in planted graph models.Comment: To appear in Proceedings of WWW 2017 (Web-science track
Fast Data in the Era of Big Data: Twitter's Real-Time Related Query Suggestion Architecture
We present the architecture behind Twitter's real-time related query
suggestion and spelling correction service. Although these tasks have received
much attention in the web search literature, the Twitter context introduces a
real-time "twist": after significant breaking news events, we aim to provide
relevant results within minutes. This paper provides a case study illustrating
the challenges of real-time data processing in the era of "big data". We tell
the story of how our system was built twice: our first implementation was built
on a typical Hadoop-based analytics stack, but was later replaced because it
did not meet the latency requirements necessary to generate meaningful
real-time results. The second implementation, which is the system deployed in
production, is a custom in-memory processing engine specifically designed for
the task. This experience taught us that the current typical usage of Hadoop as
a "big data" platform, while great for experimentation, is not well suited to
low-latency processing, and points the way to future work on data analytics
platforms that can handle "big" as well as "fast" data
THE PHYSIO ANATOMICAL VIEW OF KLEDAKA KAPHA
Dosha, Dathu and Mala form the elemental cause of human body. Our body fails to exist without these three chief constituents. The concept of Tridosha is unique contribution of Ayurveda. They are Vata, Pitta and Kapha. These constitute the anatomical and physiological aspect of human body in general. Kapha is the element which gives stability and endurance to the body. There are 5 types of Kapha according to the location and function. Kledaka is one among them. It is located in Amashaya where the major part of digestion occurs. It is anatomically the stomach. Its function is to moisten and disintegrate the ingested food particles. It also protects the stomach from self digestion. It supports other Kapha sthanas of the body. According to its location and function given in Ayurvedic classics, its role in digestion can be assessed. When we consider the functions; Kledaka Kapha can be correlated with the gastric mucus which is secreted by surface epithelial cells of gastric mucosal layer and cells of gastric glands. The functions of gastric mucus are to lubricate the food particle for the formation of chime and to protect the gastric wall.The paper is intended to explore the physio anatomical aspect of Kledaka Kapha and its action as gastric mucus and its importance in digestion and metabolism of food
The impossibility of low rank representations for triangle-rich complex networks
The study of complex networks is a significant development in modern science,
and has enriched the social sciences, biology, physics, and computer science.
Models and algorithms for such networks are pervasive in our society, and
impact human behavior via social networks, search engines, and recommender
systems to name a few. A widely used algorithmic technique for modeling such
complex networks is to construct a low-dimensional Euclidean embedding of the
vertices of the network, where proximity of vertices is interpreted as the
likelihood of an edge. Contrary to the common view, we argue that such graph
embeddings do not}capture salient properties of complex networks. The two
properties we focus on are low degree and large clustering coefficients, which
have been widely established to be empirically true for real-world networks. We
mathematically prove that any embedding (that uses dot products to measure
similarity) that can successfully create these two properties must have rank
nearly linear in the number of vertices. Among other implications, this
establishes that popular embedding techniques such as Singular Value
Decomposition and node2vec fail to capture significant structural aspects of
real-world complex networks. Furthermore, we empirically study a number of
different embedding techniques based on dot product, and show that they all
fail to capture the triangle structure
Generalization in Graph Neural Networks: Improved PAC-Bayesian Bounds on Graph Diffusion
Graph neural networks are widely used tools for graph prediction tasks.
Motivated by their empirical performance, prior works have developed
generalization bounds for graph neural networks, which scale with graph
structures in terms of the maximum degree. In this paper, we present
generalization bounds that instead scale with the largest singular value of the
graph neural network's feature diffusion matrix. These bounds are numerically
much smaller than prior bounds for real-world graphs. We also construct a lower
bound of the generalization gap that matches our upper bound asymptotically. To
achieve these results, we analyze a unified model that includes prior works'
settings (i.e., convolutional and message-passing networks) and new settings
(i.e., graph isomorphism networks). Our key idea is to measure the stability of
graph neural networks against noise perturbations using Hessians. Empirically,
we find that Hessian-based measurements correlate with the observed
generalization gaps of graph neural networks accurately. Optimizing noise
stability properties for fine-tuning pretrained graph neural networks also
improves test performance on several graph-level classification tasks.Comment: 36 pages, 2 tables, 3 figures. Appeared in AISTATS 202
An Experimental Study of Structural Diversity in Social Networks
Several recent studies of online social networking platforms have found that
adoption rates and engagement levels are positively correlated with structural
diversity, the degree of heterogeneity among an individual's contacts as
measured by network ties. One common theory for this observation is that
structural diversity increases utility, in part because there is value to
interacting with people from different network components on the same platform.
While compelling, evidence for this causal theory comes from observational
studies, making it difficult to rule out non-causal explanations. We
investigate the role of structural diversity on retention by conducting a
large-scale randomized controlled study on the Twitter platform. We first show
that structural diversity correlates with user retention on Twitter,
corroborating results from past observational studies. We then exogenously vary
structural diversity by altering the set of network recommendations new users
see when joining the platform; we confirm that this design induces the desired
changes to network topology. We find, however, that low, medium, and high
structural diversity treatment groups in our experiment have comparable
retention rates. Thus, at least in this case, the observed correlation between
structural diversity and retention does not appear to result from a causal
relationship, challenging theories based on past observational studies.Comment: To appear in the Proceedings of International AAAI Conference on Web
and Social Media (ICWSM 2020
Reducing Uncertainty in Sea-level Rise Prediction: A Spatial-variability-aware Approach
Given multi-model ensemble climate projections, the goal is to accurately and
reliably predict future sea-level rise while lowering the uncertainty. This
problem is important because sea-level rise affects millions of people in
coastal communities and beyond due to climate change's impacts on polar ice
sheets and the ocean. This problem is challenging due to spatial variability
and unknowns such as possible tipping points (e.g., collapse of Greenland or
West Antarctic ice-shelf), climate feedback loops (e.g., clouds, permafrost
thawing), future policy decisions, and human actions. Most existing climate
modeling approaches use the same set of weights globally, during either
regression or deep learning to combine different climate projections. Such
approaches are inadequate when different regions require different weighting
schemes for accurate and reliable sea-level rise predictions. This paper
proposes a zonal regression model which addresses spatial variability and model
inter-dependency. Experimental results show more reliable predictions using the
weights learned via this approach on a regional scale.Comment: 6 pages, 5 figures, I-GUIDE 2023 conferenc
FinderNet: A Data Augmentation Free Canonicalization aided Loop Detection and Closure technique for Point clouds in 6-DOF separation
We focus on the problem of LiDAR point cloud based loop detection (or
Finding) and closure (LDC) in a multi-agent setting. State-of-the-art (SOTA)
techniques directly generate learned embeddings of a given point cloud, require
large data transfers, and are not robust to wide variations in 6
Degrees-of-Freedom (DOF) viewpoint. Moreover, absence of strong priors in an
unstructured point cloud leads to highly inaccurate LDC. In this original
approach, we propose independent roll and pitch canonicalization of the point
clouds using a common dominant ground plane. Discretization of the
canonicalized point cloud along the axis perpendicular to the ground plane
leads to an image similar to Digital Elevation Maps (DEMs), which exposes
strong spatial priors in the scene. Our experiments show that LDC based on
learnt embeddings of such DEMs is not only data efficient but also
significantly more robust, and generalizable than the current SOTA. We report
significant performance gain in terms of Average Precision for loop detection
and absolute translation/rotation error for relative pose estimation (or loop
closure) on Kitti, GPR and Oxford Robot Car over multiple SOTA LDC methods. Our
encoder technique allows to compress the original point cloud by over 830
times. To further test the robustness of our technique we create and opensource
a custom dataset called Lidar-UrbanFly Dataset (LUF) which consists of point
clouds obtained from a LiDAR mounted on a quadrotor
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