Technical Perspective of Efficient Directed Densest Subgraph Discovery

2021 ◽  
Vol 50 (1) ◽  
pp. 32-32
Author(s):  
Yufei Tao
Keyword(s):  
Social Network ◽  
Graph Mining ◽  
Extensive Discussion ◽  
Dense Subgraphs ◽  
Malicious Behavior ◽  
Densest Subgraph ◽  
Building Brick

The problem is useful in graph mining because dense subgraphs often represent patterns deserving special attention. They could indicate, for example, an authoritative community in a social network, a building brick of more complex biology structures, or even a type of malicious behavior such as spamming. See [1, 3] and the references therein for an extensive discussion on the applications of DDS.

scholarly journals Finding events in temporal networks: segmentation meets densest subgraph discovery

2019 ◽  
Vol 62 (4) ◽  
pp. 1611-1639
Author(s):  
Polina Rozenshtein ◽  
Francesco Bonchi ◽  
Aristides Gionis ◽  
Mauro Sozio ◽  
Nikolaj Tatti
Keyword(s):  
Optimization Problem ◽  
Approximate Dynamic Programming ◽  
Problem Formulation ◽  
Network Activity ◽  
Total Density ◽  
Temporal Networks ◽  
Time Intervals ◽  
Dense Subgraphs ◽  
Approximation Guarantee ◽  
Densest Subgraph

Abstract In this paper, we study the problem of discovering a timeline of events in a temporal network. We model events as dense subgraphs that occur within intervals of network activity. We formulate the event discovery task as an optimization problem, where we search for a partition of the network timeline into k non-overlapping intervals, such that the intervals span subgraphs with maximum total density. The output is a sequence of dense subgraphs along with corresponding time intervals, capturing the most interesting events during the network lifetime. A naïve solution to our optimization problem has polynomial but prohibitively high running time. We adapt existing recent work on dynamic densest subgraph discovery and approximate dynamic programming to design a fast approximation algorithm. Next, to ensure richer structure, we adjust the problem formulation to encourage coverage of a larger set of nodes. This problem is NP-hard; however, we show that on static graphs a simple greedy algorithm leads to approximate solution due to submodularity. We extend this greedy approach for temporal networks, but we lose the approximation guarantee in the process. Finally, we demonstrate empirically that our algorithms recover solutions with good quality.

KClist++

2020 ◽  
Vol 13 (10) ◽  
pp. 1628-1640
Author(s):  
Bintao Sun ◽  
Maximilien Danisch ◽  
T-H. Hubert Chan ◽  
Mauro Sozio
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Real World ◽  
Simple Procedure ◽  
Maximum Flow ◽  
Exact Algorithms ◽  
Large Graphs ◽  
Recent Approach ◽  
Densest Subgraph

The problem of finding densest subgraphs has received increasing attention in recent years finding applications in biology, finance, as well as social network analysis. The k -clique densest subgraph problem is a generalization of the densest subgraph problem, where the objective is to find a subgraph maximizing the ratio between the number of k -cliques in the subgraph and its number of nodes. It includes as a special case the problem of finding subgraphs with largest average number of triangles ( k = 3), which plays an important role in social network analysis. Moreover, algorithms that deal with larger values of k can effectively find quasi-cliques. The densest subgraph problem can be solved in polynomial time with algorithms based on maximum flow, linear programming or a recent approach based on convex optimization. In particular, the latter approach can scale to graphs containing tens of billions of edges. While finding a densest subgraph in large graphs is no longer a bottleneck, the k -clique densest subgraph remains challenging even when k = 3. Our work aims at developing near-optimal and exact algorithms for the k -clique densest subgraph problem on large real-world graphs. We give a surprisingly simple procedure that can be employed to find the maximal k -clique densest subgraph in large-real world graphs. By leveraging appealing properties of existing results, we combine it with a recent approach for listing all k -cliques in a graph and a sampling scheme, obtaining the state-of-the-art approaches for the aforementioned problem. Our theoretical results are complemented with an extensive experimental evaluation showing the effectiveness of our approach in large real-world graphs.

Weighted Subgraph Mining Technology to Knowledge Discovery

2010 ◽  
Vol 20-23 ◽  
pp. 1167-1173
Author(s):  
Bo Liu
Keyword(s):  
Social Network ◽  
Microarray Data ◽  
Linear Correlation ◽  
Graph Mining ◽  
Transportation Networks ◽  
Weighted Graph ◽  
Educational Context ◽  
Community Discovery ◽  
Graph Data ◽  
Subgraph Mining

Currently there is such an increasing interest in discovering important patterns from graph data. A significant number of applications require effective and efficient manipulation of graph mining, such as being: (i) analysis of microarray data in bioinformatics, (ii) pattern discovery in a large graph representing a social network, (iii) analysis of transportation networks, (iv) community discovery in Web data. This paper concerned with subgraph discovery from weighted graph data that came from the educational context. The non-linear correlation technology was introduced and used in the mining process in the whole knowledge achieved. At last we have applied these methods in real course management datasets and found correspondent results for the educators.

scholarly journals An Attention-Based Graph Neural Network for Spam Bot Detection in Social Networks

2020 ◽  
Vol 10 (22) ◽  
pp. 8160
Author(s):  
Chensu Zhao ◽  
Yang Xin ◽  
Xuefeng Li ◽  
Hongliang Zhu ◽  
Yixian Yang ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Graph Mining ◽  
Rapid Development ◽  
Complex Method ◽  
Detection Model ◽  
Security Problem ◽  
Bot Detection ◽  
Network Platform ◽  
Mining Algorithms

With the rapid development of social networks, spam bots and other anomaly accounts’ malicious behavior has become a critical information security problem threatening the social network platform. In order to reduce this threat, the existing research mainly uses feature-based detection or propagation-based detection, and it applies machine learning or graph mining algorithms to identify anomaly accounts in social networks. However, with the development of technology, spam bots are becoming more advanced, and identifying bots is still an open challenge. This paper proposes a new semi-supervised graph embedding model based on a graph attention network for spam bot detection in social networks. This approach constructs a detection model by aggregating features and neighbor relationships, and learns a complex method to integrate the different neighborhood relationships between nodes to operate the directed social graph. The new model can identify spam bots by capturing user features and two different relationships among users in social networks. We compare our method with other methods on real-world social network datasets, and the experimental results show that our proposed model achieves a significant and consistent improvement.

Research of Privacy-Preserving for Graph Mining of Government Affairs Office Automation Systems

2014 ◽  
Vol 543-547 ◽  
pp. 3004-3008
Author(s):  
Feng Qin Zhang ◽  
Liang Dong ◽  
Shui Ping Zhang ◽  
Xiao Qing Li ◽  
Gui Rong Chen
Keyword(s):  
Social Network ◽  
Graph Mining ◽  
Office Automation ◽  
Privacy Preserving ◽  
Public Institutions ◽  
Privacy Leakage ◽  
Random Weights ◽  
Automation Systems ◽  
The Government ◽  
Important Nodes

Against the problems of privacy leakage from the graph of workflow structure of the government affair OA systems, the paper puts forwards a privacy preserving model which is effective in the protection of degrees and workflow amounts of the nodes in the government business workflow graph. For further implementation, it proposes a new unified anonymization algorithm oriented the actual nodes in the workflow graph based on the ides of social network anonymization and a new method of random weights disturbing to prevent the important nodes in the graph from being recognized by attackers and protect the information of workflow amount in the government and public institutions. At the end of this paper, the model has been verified by two experimentations that proves its feasibility and usability.

scholarly journals Sub-Community Graph Retrieval from a Compressed Community Graph using Graph Mining

2019 ◽  
Vol 9 (1) ◽  
pp. 6176-6182
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Community Detection ◽  
Graph Mining ◽  
Experimental Results ◽  
Large Community

Community detection and its retrieval is one of the most relevant and important topics in graph mining. Hence it is treated as one of the important applications in the field of social network analysis. Community detection plays an important role in a large community graph by enabling and selecting the desired community’s sub-graph. The proposed algorithm detects and extracts the desired sub-community graph from a compressed community graph for further analysis purpose. The authors present both theoretical and experimental results with three benchmark social networks. The proposed technique is efficient in terms of complexities.

An Approach to Mining Information From Telephone Graph Using Graph Mining Techniques

2018 ◽  
pp. 34-52
Author(s):  
Bapuji Rao ◽  
Sasmita Mishra ◽  
Sarojananda Mishra
Keyword(s):  
Graph Theory ◽  
Social Network ◽  
Graph Mining ◽  
Initial Section ◽  
Social Contexts ◽  
Direct Application ◽  
Related Literature ◽  
C Programming Language ◽  
C Programming ◽  
Basic Concepts

This chapter focuses on methods to study communication in a real world social network using the basic concepts of graph theory. The initial section of this chapter starts with a general introduction consisting of related literature and definitions towards understanding the basic concepts of graph mining and graph theory, defining a telephone graph and use of telephone graph for social contexts. The authors have proposed an algorithm for extracting different network provider's sub-graphs, weak and strong connected sub-graphs and extracting incoming and outgoing calls of subscribers which have direct application for studying the human behavior in telephone network. The authors have considered two examples. The authors have implemented the proposed algorithm in C++ programming language and obtained satisfactory results. Finally, the authors have included the snapshots of the output in the chapter to enhance the interest of the readers.

Graph Mining and Its Applications in Studying Community-Based Graph under the Preview of Social Network

2016 ◽  
pp. 94-146
Author(s):  
Bapuji Rao ◽  
Anirban Mitra
Keyword(s):  
Data Mining ◽  
Social Network ◽  
Information Extraction ◽  
Graph Mining ◽  
Initial Section ◽  
Social Contexts ◽  
Community Based ◽  
Related Literature ◽  
C Programming ◽  
Definition Of

One of the fundamental tasks in structured data mining is discovering of frequent sub-structures. These discovered patterns can be used for characterizing structure datasets, classifying and clustering complex structures, building graph indices & performing similarity search in large graph databases. In this chapter, the authors have discussed on use of graph techniques to identify communities and sub-communities and to derive a community structure for social network analysis, information extraction and knowledge management. This chapter contributes towards the graph mining, its application in social network using community based graph. Initial section is related literature and definition of community graph and its usage in social contexts. Detecting common community sub-graph between two community graphs comes under information extraction using graph mining technique. Examples from movie database to village administration were considered here. C++ programming is used and outputs have been included to enhance the reader's interest.

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