Results 1 - 10
of
15
Community Detection in Networks with Node Attributes
"... Abstract—Community detection algorithms are fundamental tools that allow us to uncover organizational principles in networks. When detecting communities, there are two possible sources of information one can use: the network structure, and the features and attributes of nodes. Even though communitie ..."
Abstract
-
Cited by 19 (0 self)
- Add to MetaCart
(Show Context)
Abstract—Community detection algorithms are fundamental tools that allow us to uncover organizational principles in networks. When detecting communities, there are two possible sources of information one can use: the network structure, and the features and attributes of nodes. Even though communities form around nodes that have common edges and common attributes, typically, algorithms have only focused on one of these two data modalities: community detection algorithms traditionally focus only on the network structure, while clustering algorithms mostly consider only node attributes. In this paper, we develop Com-munities from Edge Structure and Node Attributes (CESNA), an accurate and scalable algorithm for detecting overlapping communities in networks with node attributes. CESNA statis-tically models the interaction between the network structure and the node attributes, which leads to more accurate community detection as well as improved robustness in the presence of noise in the network structure. CESNA has a linear runtime in the network size and is able to process networks an order of magnitude larger than comparable approaches. Last, CESNA also helps with the interpretation of detected communities by finding relevant node attributes for each community. I.
Modeling and Detecting Community Hierarchies
"... Abstract. Community detection has in recent years emerged as an invaluable tool for describing and quantifying interactions in networks. In this paper we propose a theoretical model that explicitly formalizes both the tight connections within each community and the hierarchical nature of the communi ..."
Abstract
-
Cited by 4 (3 self)
- Add to MetaCart
(Show Context)
Abstract. Community detection has in recent years emerged as an invaluable tool for describing and quantifying interactions in networks. In this paper we propose a theoretical model that explicitly formalizes both the tight connections within each community and the hierarchical nature of the communities. We further present an efficient algorithm that provably detects all the communities in our model. Experiments demonstrate that our definition successfully models real world communities, and our algorithm compares favorably with existing approaches.
Nonparametric Multi-group Membership Model for Dynamic Networks
"... Relational data—like graphs, networks, and matrices—is often dynamic, where the relational struc-ture evolves over time. A fundamental problem in the analysis of time-varying network data is to extract a summary of the common structure and the dynamics of the underlying relations between the entitie ..."
Abstract
-
Cited by 4 (0 self)
- Add to MetaCart
(Show Context)
Relational data—like graphs, networks, and matrices—is often dynamic, where the relational struc-ture evolves over time. A fundamental problem in the analysis of time-varying network data is to extract a summary of the common structure and the dynamics of the underlying relations between the entities. Here we build on the intuition that changes in the network structure are driven by the dy-namics at the level of groups of nodes. We propose a nonparametric multi-group membership model for dynamic networks. Our model contains three main components: We model the birth and death of individual groups with respect to the dynamics of the network structure via a distance dependent In-dian Buffet Process. We capture the evolution of individual node group memberships via a Factorial Hidden Markov model. And, we explain the dynamics of the network structure by explicitly mod-eling the connectivity structure of groups. We demonstrate our model’s capability of identifying the dynamics of latent groups in a number of different types of network data. Experimental results show that our model provides improved predictive performance over existing dynamic network models on future network forecasting and missing link prediction. 1
Solving the Missing Node Problem using Structure and Attribute Information
"... Abstract—An important area of social networks research is identifying missing information which is not explicitly represented in the network, or is not visible to all. Recently, the Missing Node Identification problem was introduced where missing members in the social network structure must be ident ..."
Abstract
-
Cited by 3 (2 self)
- Add to MetaCart
(Show Context)
Abstract—An important area of social networks research is identifying missing information which is not explicitly represented in the network, or is not visible to all. Recently, the Missing Node Identification problem was introduced where missing members in the social network structure must be identified. However, previous works did not consider the possibility that information about specific users (nodes) within the network could be useful in solving this problem. In this paper, we present two algorithms: SAMI-A and SAMI-N. Both of these algorithms use the known nodes’ specific information, such as demographic information and the nodes ’ historical behavior in the network. We found that both SAMI-A and SAMI-N perform significantly better than other missing node algorithms. However, as each of these algorithms and the parameters within these algorithms often perform better in specific problem instances, a mechanism is needed to select the best algorithm and the best variation within that algorithm. Towards this challenge, we also present OASCA, a novel online selection algorithm. We present results that detail the success of the algorithms presented within this paper. I.
C.: Scalable text and link analysis with mixed-topic link models
- In: Proc. of KDD
, 2013
"... ar ..."
Features
"... Many methods have been proposed for community detection in networks, but most of them do not take into account additional information on the nodes that is often available in practice. In this paper, we propose a new joint community detection criterion that use both the network and the features to de ..."
Abstract
-
Cited by 2 (0 self)
- Add to MetaCart
(Show Context)
Many methods have been proposed for community detection in networks, but most of them do not take into account additional information on the nodes that is often available in practice. In this paper, we propose a new joint community detection criterion that use both the network and the features to detect community structure. One advantage our method has over existing joint detection approaches is the flexibility of learning the impact of different features which may differ across communities. Another advantage is the flexibility of choosing the amount of influence the feature information has on communities. The method is asymptotically consistent under the block model with additional assumptions on the feature distributions, and performs well on simulated and real networks. 1
Ranking Networks
"... Latent space models for network formation assume that nodes possess latent attributes that determine their propensity to connect. We propose a new model for network formation, ranking networks, in which these attributes are rankings over some space of alternatives. Such rankings may reflect user pre ..."
Abstract
-
Cited by 2 (2 self)
- Add to MetaCart
(Show Context)
Latent space models for network formation assume that nodes possess latent attributes that determine their propensity to connect. We propose a new model for network formation, ranking networks, in which these attributes are rankings over some space of alternatives. Such rankings may reflect user preferences, relevance/quality judgements, etc., while ranking networks capture correlations of, say, user preferences across a social network. We present preliminary theoretical and empirical analyses of structural properties of such networks, and develop algorithmic approximations to help efficiently predict these properties. Empirical results demonstrate the quality of these approximations. 1
Online Egocentric Models for Citation Networks
"... With the emergence of large-scale evolving (timevarying) networks, dynamic network analysis (DNA) has become a very hot research topic in recent years. Although a lot of DNA methods have been proposed by researchers from different communities, most of them can only model snapshot data recorded at a ..."
Abstract
-
Cited by 2 (2 self)
- Add to MetaCart
With the emergence of large-scale evolving (timevarying) networks, dynamic network analysis (DNA) has become a very hot research topic in recent years. Although a lot of DNA methods have been proposed by researchers from different communities, most of them can only model snapshot data recorded at a very rough temporal granularity. Recently, some models have been proposed for DNA which can be used to model large-scale citation networks at a fine temporal granularity. However, they suffer from a significant decrease of accuracy over time because the learned parameters or node features are static (fixed) during the prediction process for evolving citation
Privacy-Preserving Link Prediction in Decentralized Online Social Networks
"... Abstract. We consider the privacy-preserving link prediction problem in decentralized online social network (OSNs). We formulate the problem as a sparse logistic regression problem and solve it with a novel decentral-ized two-tier method using alternating direction method of multipliers (ADMM). This ..."
Abstract
-
Cited by 1 (0 self)
- Add to MetaCart
(Show Context)
Abstract. We consider the privacy-preserving link prediction problem in decentralized online social network (OSNs). We formulate the problem as a sparse logistic regression problem and solve it with a novel decentral-ized two-tier method using alternating direction method of multipliers (ADMM). This method enables end users to collaborate with their online service providers without jeopardizing their data privacy. The method also grants end users fine-grained privacy control to their personal data by supporting arbitrary public/private data split. Using real-world data, we show that our method enjoys various advantages including high pre-diction accuracy, balanced workload, and limited communication over-head. Additionally, we demonstrate that our method copes well with link reconstruction attack.
Birds of a Feather Linked Together: A Discriminative Topic Model using Link-based Priors
"... Abstract A wide range of applications, from social media to scientific literature analysis, involve graphs in which documents are connected by links. We introduce a topic model for link prediction based on the intuition that linked documents will tend to have similar topic distributions, integratin ..."
Abstract
- Add to MetaCart
(Show Context)
Abstract A wide range of applications, from social media to scientific literature analysis, involve graphs in which documents are connected by links. We introduce a topic model for link prediction based on the intuition that linked documents will tend to have similar topic distributions, integrating a max-margin learning criterion and lexical term weights in the loss function. We validate our approach on the tweets from 2,000 Sina Weibo users and evaluate our model's reconstruction of the social network.
