Exploiting higher-order patterns for community detection in attributed graphs

As a fundamental task in cluster analysis, community detection is crucial for the understanding of complex network systems in many disciplines such as biology and sociology. Recently, due to the increase in the richness and variety of attribute information associated with individual nodes, detecting communities in attributed graphs becomes a more challenging problem. Most existing works focus on the similarity between pairwise nodes in terms of both structural and attribute information while ignoring the higher-order patterns involving more than two nodes. In this paper, we explore the possibility of making use of higher-order information in attributed graphs to detect communities. To do so, we first compose tensors to specifically model the higher-order patterns of interest from the aspects of network structures and node attributes, and then propose a novel algorithm to capture these patterns for community detection. Extensive experiments on several real-world datasets with varying sizes and different characteristics of attribute information demonstrated the promising performance of our algorithm.

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Extending Proximity Measures to Attributed Networks for Community Detection

Complex Systems ◽

10.25088/complexsystems.30.4.441 ◽

2021 ◽

Vol 30 (4) ◽

pp. 441-455

Author(s):

Rinat Aynulin ◽

◽

Pavel Chebotarev ◽

◽

Keyword(s):

Network Analysis ◽

Community Detection ◽

Network Structure ◽

Real World ◽

Adjacency Matrix ◽

Attribute Information ◽

Node Attributes ◽

Proximity Measures ◽

Definition Of ◽

Attributed Networks

Proximity measures on graphs are extensively used for solving various problems in network analysis, including community detection. Previous studies have considered proximity measures mainly for networks without attributes. However, attribute information, node attributes in particular, allows a more in-depth exploration of the network structure. This paper extends the definition of a number of proximity measures to the case of attributed networks. To take node attributes into account, attribute similarity is embedded into the adjacency matrix. Obtained attribute-aware proximity measures are numerically studied in the context of community detection in real-world networks.

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Clustering attributed graphs: Models, measures and methods

Network Science ◽

10.1017/nws.2015.9 ◽

2015 ◽

Vol 3 (3) ◽

pp. 408-444 ◽

Cited By ~ 93

Author(s):

CECILE BOTHOREL ◽

JUAN DAVID CRUZ ◽

MATTEO MAGNANI ◽

BARBORA MICENKOVÁ

Keyword(s):

Social Networks ◽

Community Detection ◽

Social Systems ◽

Graph Clustering ◽

Literature Survey ◽

Clustering Methods ◽

Open Problems ◽

Attributed Graphs ◽

Partial Representation ◽

Node Attributes

AbstractClustering a graph, i.e., assigning its nodes to groups, is an important operation whose best known application is the discovery of communities in social networks. Graph clustering and community detection have traditionally focused on graphs without attributes, with the notable exception of edge weights. However, these models only provide a partial representation of real social systems, that are thus often described using node attributes, representing features of the actors, and edge attributes, representing different kinds of relationships among them. We refer to these models asattributed graphs. Consequently, existing graph clustering methods have been recently extended to deal with node and edge attributes. This article is a literature survey on this topic, organizing, and presenting recent research results in a uniform way, characterizing the main existing clustering methods and highlighting their conceptual differences. We also cover the important topic of clustering evaluation and identify current open problems.

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Overlapping Community Detection Based on Attribute Augmented Graph

Entropy ◽

10.3390/e23060680 ◽

2021 ◽

Vol 23 (6) ◽

pp. 680

Author(s):

Hanyang Lin ◽

Yongzhao Zhan ◽

Zizheng Zhao ◽

Yuzhong Chen ◽

Chen Dong

Keyword(s):

Community Detection ◽

Real World ◽

Detection Algorithm ◽

Overlapping Community Detection ◽

Overlapping Communities ◽

Adjustment Strategy ◽

Topology Information ◽

Overlapping Community ◽

Real World Datasets ◽

Community Detection Algorithm

There is a wealth of information in real-world social networks. In addition to the topology information, the vertices or edges of a social network often have attributes, with many of the overlapping vertices belonging to several communities simultaneously. It is challenging to fully utilize the additional attribute information to detect overlapping communities. In this paper, we first propose an overlapping community detection algorithm based on an augmented attribute graph. An improved weight adjustment strategy for attributes is embedded in the algorithm to help detect overlapping communities more accurately. Second, we enhance the algorithm to automatically determine the number of communities by a node-density-based fuzzy k-medoids process. Extensive experiments on both synthetic and real-world datasets demonstrate that the proposed algorithms can effectively detect overlapping communities with fewer parameters compared to the baseline methods.

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Review Summary Generation in Online Systems: Frameworks for Supervised and Unsupervised Scenarios

ACM Transactions on the Web ◽

10.1145/3448015 ◽

2021 ◽

Vol 15 (3) ◽

pp. 1-33

Author(s):

Wenjun Jiang ◽

Jing Chen ◽

Xiaofei Ding ◽

Jie Wu ◽

Jiawei He ◽

...

Keyword(s):

Decision Making ◽

Real World ◽

Text Summarization ◽

Experimental Results ◽

Product Review ◽

Comprehensive Review ◽

Online Systems ◽

Real World Datasets ◽

Different Characteristics

In online systems, including e-commerce platforms, many users resort to the reviews or comments generated by previous consumers for decision making, while their time is limited to deal with many reviews. Therefore, a review summary, which contains all important features in user-generated reviews, is expected. In this article, we study “how to generate a comprehensive review summary from a large number of user-generated reviews.” This can be implemented by text summarization, which mainly has two types of extractive and abstractive approaches. Both of these approaches can deal with both supervised and unsupervised scenarios, but the former may generate redundant and incoherent summaries, while the latter can avoid redundancy but usually can only deal with short sequences. Moreover, both approaches may neglect the sentiment information. To address the above issues, we propose comprehensive Review Summary Generation frameworks to deal with the supervised and unsupervised scenarios. We design two different preprocess models of re-ranking and selecting to identify the important sentences while keeping users’ sentiment in the original reviews. These sentences can be further used to generate review summaries with text summarization methods. Experimental results in seven real-world datasets (Idebate, Rotten Tomatoes Amazon, Yelp, and three unlabelled product review datasets in Amazon) demonstrate that our work performs well in review summary generation. Moreover, the re-ranking and selecting models show different characteristics.

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Deep autoencoder-based community detection in complex networks with particle swarm optimization and continuation algorithms

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-201342 ◽

2021 ◽

pp. 1-17

Author(s):

Mohammed Al-Andoli ◽

Wooi Ping Cheah ◽

Shing Chiang Tan

Keyword(s):

Particle Swarm Optimization ◽

Complex Networks ◽

Learning Community ◽

Community Detection ◽

Particle Swarm ◽

Premature Convergence ◽

Swarm Optimization ◽

Detection Algorithms ◽

Real World Datasets ◽

The Cost

Detecting communities is an important multidisciplinary research discipline and is considered vital to understand the structure of complex networks. Deep autoencoders have been successfully proposed to solve the problem of community detection. However, existing models in the literature are trained based on gradient descent optimization with the backpropagation algorithm, which is known to converge to local minima and prove inefficient, especially in big data scenarios. To tackle these drawbacks, this work proposed a novel deep autoencoder with Particle Swarm Optimization (PSO) and continuation algorithms to reveal community structures in complex networks. The PSO and continuation algorithms were utilized to avoid the local minimum and premature convergence, and to reduce overall training execution time. Two objective functions were also employed in the proposed model: minimizing the cost function of the autoencoder, and maximizing the modularity function, which refers to the quality of the detected communities. This work also proposed other methods to work in the absence of continuation, and to enable premature convergence. Extensive empirical experiments on 11 publically-available real-world datasets demonstrated that the proposed method is effective and promising for deriving communities in complex networks, as well as outperforming state-of-the-art deep learning community detection algorithms.

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Heterogeneous Influence Maximization Through Community Detection in Social Networks

International Journal of Ambient Computing and Intelligence ◽

10.4018/ijaci.2021100107 ◽

2021 ◽

Vol 12 (4) ◽

pp. 118-131

Author(s):

Jaya Krishna Raguru ◽

Devi Prasad Sharma

Keyword(s):

Community Detection ◽

Greedy Algorithms ◽

Computational Cost ◽

Optimal Solution ◽

Influence Maximization ◽

Centrality Measures ◽

Influence Spread ◽

Real World Datasets ◽

Initial Seed ◽

High Computational Cost

The problem of identifying a seed set composed of K nodes that increase influence spread over a social network is known as influence maximization (IM). Past works showed this problem to be NP-hard and an optimal solution to this problem using greedy algorithms achieved only 63% of spread. However, this approach is expensive and suffered from performance issues like high computational cost. Furthermore, in a network with communities, IM spread is not always certain. In this paper, heterogeneous influence maximization through community detection (HIMCD) algorithm is proposed. This approach addresses initial seed nodes selection in communities using various centrality measures, and these seed nodes act as sources for influence spread. A parallel influence maximization is applied with the aid of seed node set contained in each group. In this approach, graph is partitioned and IM computations are done in a distributed manner. Extensive experiments with two real-world datasets reveals that HCDIM achieves substantial performance improvement over state-of-the-art techniques.

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“Yay! Yuck!” Toddlers use incongruent emotions to reason about hidden objects

10.31234/osf.io/zm6p7 ◽

2020 ◽

Author(s):

Alexis Sierra Smith-Flores ◽

Lisa Feigenson

Keyword(s):

Early Development ◽

Present Experiment ◽

Single Agent ◽

Emotional Responses ◽

Higher Order ◽

Single Stimulus ◽

Developmental Trajectory ◽

Infants And Toddlers ◽

The World ◽

Do So

Infants show impressive sensitivity to others’ emotions from early on, attending to and discriminating different facial emotions, using emotions to decide what to approach or avoid, and recognizing that certain objects and events are likely to produce certain emotional responses. But do infants and toddlers also recognize more abstract features of emotions—features that are not tied to any one emotion in particular? Here we examined the development of the higher order expectation that emotions are more or less mutually exclusive, asking whether young children recognize that people generally do not express two conflicting emotions towards a single stimulus. We first asked whether 26-month old toddlers can use an agent’s incongruent versus congruent emotional responses (“Yay! Yuck!” versus “Yay! Wow!”) to reason about how many objects were hidden in a box. We found that toddlers inferred that incongruent emotions signaled the presence of two numerically distinct objects (Experiment 1). This inference relied on the incongruent emotions being produced by a single agent; when two different agents gave two incongruent emotional responses, toddlers did not assume that two objects must be present (Experiment 2). Finally, we examined the developmental trajectory of this ability. We found that younger, 20-month olds failed to use incongruent emotions to individuate objects (Experiment 3), although they readily used incongruent novel labels to do so (Experiment 4). Our results suggest that by 2-years of age, children use higher order knowledge of emotions to make inferences about the world around them, and that this ability undergoes early development.

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Incrementality and intention-recognition in utterance processing

Dialogue & Discourse ◽

10.5087/dad.2011.109 ◽

2011 ◽

Vol 2 (1) ◽

pp. 199-233 ◽

Cited By ~ 25

Author(s):

Eleni Gregoromichelaki ◽

Ruth Kempson ◽

Matthew Purver ◽

Gregory J. Mills ◽

Ronnie Cann ◽

...

Keyword(s):

Predictive Model ◽

Language Processing ◽

Mental States ◽

Higher Order ◽

Low Level ◽

Intention Recognition ◽

Dialogue Modelling ◽

Open Question ◽

Dynamic Syntax ◽

Do So

Ever since dialogue modelling first developed relative to broadly Gricean assumptions about utter-ance interpretation (Clark, 1996), it has remained an open question whether the full complexity of higher-order intention computation is made use of in everyday conversation. In this paper we examine the phenomenon of split utterances, from the perspective of Dynamic Syntax, to further probe the necessity of full intention recognition/formation in communication: we do so by exploring the extent to which the interactive coordination of dialogue exchange can be seen as emergent from low-level mechanisms of language processing, without needing representation by interlocutors of each other’s mental states, or fully developed intentions as regards messages to be conveyed. We thus illustrate how many dialogue phenomena can be seen as direct consequences of the grammar architecture, as long as this is presented within an incremental, goal-directed/predictive model.

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