In the Web3 on-chain ecosystem, on-chain behavior is always in dynamic change—trading frequency surges in bull markets, cross-chain operations are frequent, while in bear markets, address behavior tends to be conservative and distribution strategies are more concealed; project parties will also adjust funding links according to the regulatory environment, avoiding risks through dynamically changing interaction habits. However, traditional on-chain association analysis tools have long adopted a 'static analysis paradigm': fixed feature weights, solidified risk thresholds, static cross-chain comparisons, which cannot adapt to the dynamic attributes of on-chain behavior, resulting in a significant fluctuation in association recognition accuracy with market changes, and even causing 'misjudgment of compliant addresses during bull markets and omission of hidden distributions during bear markets'. The core innovation of Bubblemaps lies in constructing a 'dynamic adaptability system for on-chain association analysis', enabling the association analysis capability to autonomously adjust with changes in on-chain behavior through 'dynamic feature weights, real-time evidence iteration, cross-chain dynamic collaboration', redefining the adaptation standards of on-chain association analysis for dynamic ecosystems.
I. The core limitations of traditional static analysis paradigms: Why has dynamic adaptability become a necessity?
The 'static paradigm' of traditional on-chain association analysis tools is fundamentally designed based on the 'unchanging on-chain behavior assumption', and its core limitations have become increasingly prominent with the complexity of the Web3 ecosystem, specifically manifested in three major contradictions:
1. The contradiction between fixed feature weights and behavioral dynamics
Traditional tools apply 'fixed weight allocation' to 23-dimensional behavior features (e.g., trading frequency, Gas fee patterns, contract interaction preferences), for instance, regardless of market cycles, always setting the weight of 'trading frequency' to 15% and 'Gas fee patterns' to 12%. However, in actual on-chain behavior, the core feature differences in different cycles are significant: in bull markets, user trading frequency generally increases, and 'trading frequency synchronization' is no longer the core identifier for associated addresses (many ordinary users also engage in high-frequency trading). If high weight is maintained, compliant individual addresses may be misjudged as associated distributions; in bear markets, addresses pay more attention to 'behavioral concealment', and the stability of Gas fee patterns and contract interaction preferences becomes key for association identification; fixed weights may result in omissions due to 'neglecting core features'. An industry report shows that the misjudgment rate for association recognition by traditional tools reached 45% during bull markets, and the omission rate exceeded 38% during bear markets, with the core reason being the inability of fixed weights to adapt to behavioral dynamics.
2. The contradiction between solidified risk thresholds and scenario diversity
Traditional tools adopt a 'one-size-fits-all' design for risk conclusion thresholds, for example, irrespective of token type (Meme coins, DeFi tokens, RWA tokens) or public chain characteristics (high Gas fees on Ethereum, high TPS on Solana), setting 'behavior fingerprint similarity > 80%' as the association threshold and 'holding concentration > 60%' as the control risk threshold. However, the reasonable threshold differences in different scenarios are vast: for Meme coins, due to active community trading, a holding concentration of 60% may be normal market behavior, while the same proportion for DeFi tokens likely indicates control risk; Ethereum addresses, due to high Gas cost, have low cross-chain operation frequency, while Solana addresses commonly exhibit cross-chain behavior. Using the same cross-chain association threshold would lead to omissions for Ethereum addresses and misjudgments for Solana addresses. Certain test data shows that traditional tools' misjudgment rate for control risk of Meme coins reached 32%, and the omission rate for Solana cross-chain addresses exceeded 29%, with the root cause being the inability of fixed thresholds to adapt to scenario diversity.
3. The contradiction between static cross-chain comparisons and dynamic identities
Traditional tools' cross-chain association analysis relies on 'static attribute comparison of addresses' (e.g., initial funding sources, historical transfer records), making it unable to capture 'dynamic changes in cross-chain behavior': for instance, a certain address using a fixed Gas fee level on Ethereum may deliberately change trading hours and fee habits after crossing to Solana to avoid detection. Traditional static comparisons cannot identify its cross-chain associated identity due to 'behavior feature mismatches'; some project parties even periodically change cross-chain bridges, cutting off historical funding links, and static cross-chain comparisons become fragmented due to 'relying on old link data'. In Q1 2024, in a cross-chain money laundering case, traditional tools' inability to identify 'cross-chain addresses after behavior changes' led to an extension of the tracking period by 21 days, with the core issue being static comparisons' failure to adapt to dynamic identities.
II. The Dynamic Adaptability System of Bubblemaps: Technical Architecture and Core Logic
The dynamic adaptability system of Bubblemaps is not merely a simple 'parameter adjustment', but rather builds a closed-loop capability of 'perception-adjustment-validation' based on the technical foundation already implemented by the project (23-dimensional behavior fingerprints, Intel Desk distributed validation, cross-chain feature mapping), allowing association analysis to dynamically evolve with on-chain behavior, core including three major technical modules:
1. Dynamic Feature Weight Module: Autonomous adjustment of weights based on behavioral trends
Projects develop 'market cycle perception algorithms' and 'feature importance assessment models' to achieve dynamic allocation of weights for 23-dimensional behavior features, addressing the contradiction between 'fixed weights and behavioral dynamics':
• Market Cycle Perception: The algorithm automatically determines the current market cycle (bull market, bear market, sideways market) by capturing the three key indicators of 'overall market trading frequency, cross-chain operation volume, and Gas fee volatility' in real-time: when the overall market trading frequency increases by over 50% week-on-week, and the cross-chain operation volume increases by over 30%, it is determined to be a bull market; when the two indicators decrease by over 40% week-on-week, a bear market is determined; otherwise, it is a sideways market. During the bull market in Q1 2024, this algorithm accurately identified the cycle based on real-time data, providing a foundation for weight adjustments.
• Feature Importance Assessment: Based on the results of cycle determination, the model autonomously adjusts feature weights: in bull markets, reduces the weights of 'trading frequency', 'cross-chain operation frequency', and other 'universal features' (from 15% to 8%, 10% to 5%), while increasing the weights of 'fund flow closure' and 'contract interaction depth' and other 'concealed features' (from 10% to 18%, 8% to 15%), avoiding misjudgment of compliant individuals; in bear markets, reverse adjustments are made, focusing on enhancing the weights of 'Gas fee pattern stability' and 'interaction contract concentration' (from 12% to 20%, 7% to 14%), strengthening the identification of concealed distributions.
• Scenario-based Weight Adaptation: Apart from market cycles, the model will further fine-tune weights based on 'token type' and 'public chain characteristics': for Meme coins, reduce the weight of 'holding concentration' (from 20% to 12%) and increase the weight of 'community interaction dispersion' (from 5% to 10%); for high TPS public chains like Solana, increase the weight of 'transaction timing accuracy' (from 9% to 16%) to adapt to their high-frequency trading characteristics. Certain test data shows that after adopting dynamic weights, the tool's misjudgment rate during bull markets dropped to 12%, and the omission rate during bear markets dropped to 9%, significantly outperforming traditional static tools.
2. Real-time Evidence Iteration Module: Updating evidence chains with on-chain behavior
Traditional tools generate evidence chains that become solidified and cannot update with changes in address behavior, leading to 'outdated evidence misleading conclusions'. Bubblemaps, based on the distributed validation mechanism of Intel Desk, constructs a 'real-time evidence iteration module' to achieve dynamic updates of evidence chains with on-chain behavior:
• Behavior Change Perception: The system monitors in real-time the 23-dimensional behavior feature changes of the associated evidence chain already in the database. When the fluctuation of a certain feature exceeds a preset threshold (e.g., Gas fee pattern similarity drops from 92% to 75%, contract interaction overlap drops from 88% to 60%), it automatically triggers an 'evidence re-examination alert'.
• Distributed Revalidation: After an alert is triggered, the system synchronizes 'behavior change data' to the Intel Desk, launching a double-blind revalidation with 100 community validators (the process is the same as the initial validation): If the validation result shows that 'associated features still meet above central confidence' (e.g., core evidence is missing but supporting evidence is still sufficient), then the feature data in the evidence chain is updated, retaining the association conclusion; if the validation result shows that 'associated features drop to low confidence' (e.g., core evidence is completely missing), the evidence chain is marked as 'invalid', and the risk conclusion is updated. In Q2 2024, a project party changed its behavioral habits due to regulatory pressure, and the system iterated in real-time to reduce its associated evidence chain from 'high confidence' to 'invalid', avoiding misjudgment.
• Evidence Version Management: All evidence chains after iteration retain historical versions, marked with 'iteration time, reasons for behavior changes, validation results', allowing users to trace back the complete evolution process of the evidence chain through a 'timeline', for example, to view the behavioral change trajectory of an address from 'associated' to 'non-associated', ensuring the traceability of conclusions. As of Q4 2024, Intel Desk has completed over 3200 real-time evidence iterations, with an accuracy rate of 99.1% for erroneous evidence iterations, consistent with the initial validation accuracy.
3. Cross-chain Dynamic Collaboration Module: Tracking dynamically changing cross-chain identities
To address the issue of 'static cross-chain comparisons being unable to adapt to dynamic identities', the project upgraded the cross-chain feature mapping algorithm, constructing a 'cross-chain dynamic collaboration module' to achieve accurate identification of 'cross-chain addresses after behavior changes':
• Real-time Cross-chain Behavior Synchronization: The system synchronizes the behavior feature changes of identified cross-chain associated addresses in real-time. For example, if address A (Ethereum) changes its Gas fee habits, the system will synchronize the changes to the feature repository of its Solana associated address B within 10 minutes, updating B's 'behavior fingerprint hash' to avoid disconnections due to 'feature mismatches'.
• Dynamic Cross-chain Link Tracking: When a cross-chain address changes interaction habits (e.g., changing cross-chain bridges, adjusting trading hours), the system uses the 'behavior similarity completion algorithm' to complete the associated links based on 'unchanged core features' (e.g., initial funding source, long-term contract interaction preferences). For example, a certain address changed its Gas fee pattern after crossing chains, but the core feature of 'long-term interaction with only 3 DeFi protocols' remained unchanged, and the system completed the associated links based on that feature, identifying its cross-chain identity.
• Cross-chain Risk Dynamic Synchronization: If the risk level of a related address on a public chain changes (e.g., from 'low risk' to 'high risk'), the system will synchronize the risk label in real-time to all its cross-chain related addresses and push alerts. In Q3 2024, after a high-risk Ethereum address crossed to Polygon, the system synchronized the risk label within 15 minutes through dynamic collaboration, helping the exchange to timely intercept the token deposit from that address and avoid losses.
III. The Industry Implementation of the Dynamic Adaptability System: Full-Scenario Value from Risk Control to Compliance
The dynamic adaptability system of Bubblemaps has been implemented in core scenarios such as auditing agencies, exchanges, and project parties, solving the adaptation challenges of traditional static tools, with value manifested in three dimensions:
1. Auditing Agencies: Enhancing the stability of cross-cycle risk identification
Traditional auditing agencies need to manually adjust analysis strategies during different market cycles, leading to low efficiency and poor consistency. After integrating with Bubblemaps' dynamic system:
• Audit Efficiency Improvement: The system automatically adapts to market cycles and token types, eliminating the need for audit personnel to manually adjust parameters. The on-chain association audit time for a certain auditing agency was reduced from an average of 48 hours to 24 hours, improving efficiency by 50%;
• Audit Consistency Assurance: Dynamic weights and real-time iterations ensure the uniformity of audit standards across different cycles and scenarios. The discrepancy rate of a certain institution's cross-cycle audit conclusions dropped from 35% to 9%, avoiding contradictions in audit conclusions between bull and bear markets;
• Enhanced Risk Coverage: In bear markets, the identification rate for concealed distributions increases by 42%, while the misjudgment rate for compliant addresses in bull markets decreases by 38%, significantly enhancing the industry recognition of audit reports.
2. Exchanges: Optimizing risk interception accuracy in dynamic scenarios
Exchanges face the core challenge of 'dynamic risks that change with the market' (e.g., high-frequency washing in bull markets, concealed dumping in bear markets), and the dynamic system provides them with precise interception capabilities:
• Real-time Threshold Adjustment: The system adjusts associated risk thresholds according to market cycles. In bull markets, it raises the 'behavior fingerprint similarity' association threshold from 80% to 85% to avoid misjudging individual addresses; in bear markets, it lowers it to 75% to strengthen the identification of concealed distributions. The risk token interception accuracy rate of a certain exchange improved by 37%;
• Cross-chain Risk Synchronization: Cross-chain dynamic collaboration ensures real-time synchronization of multi-chain risk labels. A certain exchange used this feature to update the risk label 10 minutes before the recharge of a certain cross-chain money laundering address, successfully intercepting over $8 million of involved funds;
• Survival Period Monitoring Adaptation: For tokens that are already launched, the system iterates the associated evidence chain in real-time. A certain exchange monitored the dynamic change of 'a certain Meme coin association cluster from dispersed to concentrated', issuing a warning about dumping risk 3 days in advance, helping users reduce losses.
3. Project Parties: Reducing operational costs of dynamic compliance
Project parties need to adjust funding links and behavior strategies according to the regulatory environment and market cycles, and the dynamic system provides them with the capability for 'dynamic self-inspection':
• Compliance Strategy Adaptation: Project parties can view 'compliance thresholds for different cycles' through the system, for example, adjusting trading frequency during bull markets to avoid misjudgment, optimizing the distribution strategy during bear markets to meet association standards, a certain RWA project reduced the compliance adjustment cycle from 15 days to 5 days through dynamic self-inspection;
• Real-time Risk Alerts: The system monitors changes in association evidence for project addresses in real-time. If a certain address may be misjudged as high risk due to behavioral changes, alerts are pushed in advance. A certain DeFi project adjusted its interaction habits based on this to avoid misjudgment of association risk during audits;
• Cross-chain Compliance Collaboration: Cross-chain dynamic collaboration ensures the compliance status synchronization of multi-chain addresses. After a project adjusted its funding chain on Ethereum, the system synchronized in real-time to the Solana address, avoiding cross-chain compliance loopholes.
IV. Industry Insights: Dynamic adaptability is the next-generation core capability of on-chain analysis
The practice of Bubblemaps' dynamic adaptability system provides key insights for the on-chain analysis field in Web3: the essence of the Web3 ecosystem is 'dynamic evolution', and on-chain behavior continues to change with market cycles, regulatory environments, and technological iterations. If on-chain analysis tools remain in a 'static paradigm', their value will continue to diminish as ecosystem complexity increases. Dynamic adaptability is no longer an 'add-on', but a 'survival necessity' for on-chain association analysis tools.
Currently, Web3 is in a stage of 'ecological diversification, behavioral complexity, and refined regulation', with an increasing demand for the 'dynamic adaptability' of on-chain analysis: from Meme coins to RWA tokens, from Ethereum to Layer 2, the behavioral feature differences in different scenarios are significant; project parties' avoidance strategies have also upgraded from 'static distribution' to 'dynamic behavior changes', making traditional static tools increasingly difficult to cope with. The practice of Bubblemaps demonstrates that only by enabling association analysis to possess the capabilities of 'perceiving dynamics, autonomously adjusting, and real-time iterating' can it truly adapt to the dynamic ecology of Web3 and become a reliable infrastructure for risk control and compliance auditing.
In the future, with the integration of AI technology and dynamic systems (e.g., predicting behavioral change trends through machine learning), the dynamic adaptability of on-chain association analysis will further upgrade from 'passive adaptation' to 'active prediction'. As the pioneer of the dynamic adaptability paradigm, Bubblemaps' core value lies not only in technological innovation but also in directing the industry— the next-generation core competitiveness of on-chain analysis lies in the capacity to adapt to dynamic ecosystems.
